Management of distributed computing framework components

ABSTRACT

Systems and methods are described for establishing and managing components of a distributed computing framework implemented in a data intake and query system. The distributed computing framework may include a master and a plurality of worker nodes. The master may selectively operate on a search head captain that is chosen from the search heads of the data intake and query system. The search head captain may distribute configuration information for the master and the distributed computing framework to the other search heads, which in turn, may distribute that configuration information to indexers of the data intake and query system. Worker nodes may be selectively activated for operation on the indexers based on the configuration information, and the worker nodes may additionally use the configuration information to contact the master and join the distributed computing framework. This approach may provide numerous benefits, including improved security, flexibility in the selection of worker nodes, and redundancy for failures of physical components of the data intake and query system.

RELATED APPLICATIONS

Any and all applications for which a foreign or domestic priority claimis identified in the Application Data Sheet as filed with the presentapplication are incorporated by reference under 37 CFR 1.57 and made apart of this specification.

In addition, each of the following U.S. applications is herebyincorporated by reference in its entirety herein and made a part of thisspecification:

Application No. Title Filing Date 15/276,717 DATA FABRIC SERVICE SYSTEMSep. 26, 2016 ARCHITECTURE 15/665,159 MULTI-LAYER PARTITION ALLOCATIONFOR Jul. 31, 2017 QUERY EXECUTION 15/665,148 QUERY PROCESSING USINGQUERY-RESOURCE Jul. 31, 2017 USAGE AND NODE UTILIZATION DATA 15/665,187RESOURCE ALLOCATION FOR MULTIPLE Jul. 31, 2017 DATASETS 15/665,248EXTERNAL DATASET CAPABILITY Jul. 31, 2017 COMPENSATION 15/665,197 DATACONDITIONING FOR DATASET Jul. 31, 2017 DESTINATION 15/665,279 QUERYACCELERATION DATA STORE Jul. 31, 2017 15/665,302 DYNAMIC RESOURCEALLOCATION FOR COMMON Jul. 31, 2017 STORAGE QUERY 15/665,339 DYNAMICRESOURCE ALLOCATION FOR REAL- Jul. 31, 2017 TIME SEARCH 16/051,197GENERATING A SUBQUERY FOR A DISTINCT Jul. 31, 2018 DATA INTAKE AND QUERYSYSTEM 16/051,215 SUBQUERY GENERATION BASED ON A DATA Jul. 31, 2018INGEST ESTIMATE OF AN EXTERNAL DATA SYSTEM 16/051,203 SUBQUERYGENERATION BASED ON SEARCH Jul. 31, 2018 CONFIGURATION DATA FROM ANEXTERNAL DATA SYSTEM 16/051,223 DISTRIBUTING PARTIAL RESULTS TO WORKERJul. 31, 2018 NODES FROM AN EXTERNAL DATA SYSTEM 16/051,304 DISTRIBUTINGPARTIAL RESULTS FROM AN Jul. 31, 2018 EXTERNAL DATA SYSTEM BETWEENWORKER NODES 16/051,300 TASK DISTRIBUTION IN AN EXECUTION NODE OF Jul.31, 2018 A DISTRIBUTED EXECUTION ENVIRONMENT 16/051,310 EXECUTION OF AQUERY RECEIVED FROM A Jul. 31, 2018 DATA INTAKE AND QUERY SYSTEM16/147,165 GENERATING A SUBQUERY FOR AN EXTERNAL Sep. 28, 2018 DATASYSTEM USING A CONFIGURATION FILE 16/146,990 CONVERTING AND MODIFYING ASUBQUERY Sep. 28, 2018 FOR AN EXTERNAL DATA SYSTEM 16/398,038 BUCKETDATA DISTRIBUTION FOR EXPORTING Apr. 29, 2019 DATA TO WORKER NODES16/397,970 PARTITIONING AND REDUCING RECORDS AT Apr. 29, 2019 INGEST OFA WORKER NODE 16/398,044 DETERMINING RECORDS GENERATED BY A Apr. 29,2019 PROCESSING TASK OF A QUERY 16/397,930 DETERMINING A RECORDGENERATION Apr. 29, 2019 ESTIMATE OF A PROCESSING TASK 16/398,031 QUERYSCHEDULING BASED ON A QUERY- Apr. 29, 2019 RESOURCE ALLOCATION ANDRESOURCE AVAILABILITY 16/397,968 RECORD EXPANSION AND REDUCTION BASEDApr. 29, 2019 ON A PROCESSING TASK IN A DATA INTAKE AND QUERY SYSTEM16/397,922 ASSIGNING PROCESSING TASKS IN A DATA Apr. 29, 2019 INTAKE ANDQUERY SYSTEM PCT/CN2019/085042 SEARCH TIME ESTIMATE IN A DATA INTAKEApr. 29, 2019 AND QUERY SYSTEM 16/657,916 SUPPORTING ADDITIONAL QUERYLANGUAGES Oct. 18, 2019 THROUGH DISTRIBUTED EXECUTION OF QUERY ENGINES16/657,872 QUERY EXECUTION AT A REMOTE Oct. 18, 2019 HETEROGENEOUS DATASTORE OF A DATA FABRIC SERVICE 16/657,894 REASSIGNING PROCESSING TASKSTO AN Oct. 18, 2019 EXTERNAL STORAGE SYSTEM 16/657,867 ADDRESSING MEMORYLIMITS FOR PARTITION Oct. 18, 2019 TRACKING AMONG WORKER NODES16/657,899 MANAGEMENT OF DISTRIBUTED COMPUTING Oct. 18, 2019 FRAMEWORKCOMPONENTS IN A DATA FABRIC SERVICE SYSTEM

FIELD

At least one embodiment of the present disclosure pertains to one ormore tools for facilitating searching and analyzing large sets of datato locate data of interest.

BACKGROUND

Information technology (IT) environments can include diverse types ofdata systems that store large amounts of diverse data types generated bynumerous devices. For example, a big data ecosystem may includedatabases such as MySQL and Oracle databases, cloud computing servicessuch as Amazon web services (AWS), and other data systems that storepassively or actively generated data, including machine-generated data(“machine data”). The machine data can include performance data,diagnostic data, or any other data that can be analyzed to diagnoseequipment performance problems, monitor user interactions, and to deriveother insights.

The large amount and diversity of data systems containing large amountsof structured, semi-structured, and unstructured data relevant to anysearch query can be massive, and continues to grow rapidly. Thistechnological evolution can give rise to various challenges in relationto managing, understanding and effectively utilizing the data. To reducethe potentially vast amount of data that may be generated, some datasystems pre-process data based on anticipated data analysis needs. Inparticular, specified data items may be extracted from the generateddata and stored in a data system to facilitate efficient retrieval andanalysis of those data items at a later time. At least some of theremainder of the generated data is typically discarded duringpre-processing.

However, storing massive quantities of minimally processed orunprocessed data (collectively and individually referred to as “rawdata”) for later retrieval and analysis is becoming increasingly morefeasible as storage capacity becomes more inexpensive and plentiful. Ingeneral, storing raw data and performing analysis on that data later canprovide greater flexibility because it enables an analyst to analyze allof the generated data instead of only a fraction of it.

Although the availability of vastly greater amounts of diverse data ondiverse data systems provides opportunities to derive new insights, italso gives rise to technical challenges to search and analyze the data.Tools exist that allow an analyst to search data systems separately andcollect results over a network for the analyst to derive insights in apiecemeal manner. However, UI tools that allow analysts to quicklysearch and analyze large set of raw machine data to visually identifydata subsets of interest, particularly via straightforward andeasy-to-understand sets of tools and search functionality do not exist.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is illustrated by way of example, and notlimitation, in the figures of the accompanying drawings, in which likereference numerals indicate similar elements and in which:

FIG. 1A is a block diagram of an example environment in which anembodiment may be implemented;

FIG. 1B is a block diagram of an example networked computer environment,in accordance with example embodiments;

FIG. 2 is a block diagram of an example data intake and query system, inaccordance with example embodiments;

FIG. 3 is a block diagram of an example cloud-based data intake andquery system, in accordance with example embodiments;

FIG. 4 is a block diagram of an example data intake and query systemthat performs searches across external data systems, in accordance withexample embodiments;

FIG. 5A is a flowchart of an example method that illustrates howindexers process, index, and store data received from forwarders, inaccordance with example embodiments;

FIG. 5B is a block diagram of a data structure in which time-stampedevent data can be stored in a data store, in accordance with exampleembodiments;

FIG. 5C provides a visual representation of the manner in which apipelined search language or query operates, in accordance with exampleembodiments;

FIG. 6A is a flow diagram of an example method that illustrates how asearch head and indexers perform a search query, in accordance withexample embodiments;

FIG. 6B provides a visual representation of an example manner in which apipelined command language or query operates, in accordance with exampleembodiments;

FIG. 7A is a diagram of an example scenario where a common customeridentifier is found among log data received from three disparate datasources, in accordance with example embodiments;

FIG. 7B illustrates an example of processing keyword searches and fieldsearches, in accordance with disclosed embodiments;

FIG. 7C illustrates an example of creating and using an inverted index,in accordance with example embodiments;

FIG. 7D depicts a flowchart of example use of an inverted index in apipelined search query, in accordance with example embodiments;

FIG. 8A is an interface diagram of an example user interface for asearch screen, in accordance with example embodiments;

FIG. 8B is an interface diagram of an example user interface for a datasummary dialog that enables a user to select various data sources, inaccordance with example embodiments;

FIGS. 9, 10, 11A, 11B, 11C, 11D, 12, 13, 14, and 15 are interfacediagrams of example report generation user interfaces, in accordancewith example embodiments;

FIG. 16 is an example search query received from a client and executedby search peers, in accordance with example embodiments;

FIG. 17A is an interface diagram of an example user interface of a keyindicators view, in accordance with example embodiments;

FIG. 17B is an interface diagram of an example user interface of anincident review dashboard, in accordance with example embodiments;

FIG. 17C is a tree diagram of an example a proactive monitoring tree, inaccordance with example embodiments;

FIG. 17D is an interface diagram of an example a user interfacedisplaying both log data and performance data, in accordance withexample embodiments;

FIG. 18 is a system diagram illustrating a data fabric service systemarchitecture (“DFS system”) in which an embodiment may be implemented;

FIG. 19 is an operation flow diagram illustrating an example of anoperation flow of a DFS system according to some embodiments of thepresent disclosure;

FIG. 20 is an operation flow diagram illustrating an example of aparallel export operation performed in a DFS system according to someembodiments of the present disclosure;

FIG. 21 is a flow diagram illustrating a method performed by the DFSsystem to obtain time-ordered search results according to someembodiments of the present disclosure;

FIG. 22 is a flow diagram illustrating a method performed by a dataintake and query system of a DFS system to obtain time-ordered searchresults according to some embodiments of the present disclosure;

FIG. 23 is a flow diagram illustrating a method performed by nodes of aDFS system to obtain batch or reporting search results according to someembodiments of the present disclosure;

FIG. 24 is a flow diagram illustrating a method performed by a dataintake and query system of a DFS system in response to a reportingsearch query according to some embodiments of the present disclosure;

FIG. 25 is a system diagram illustrating a co-located deployment of aDFS system in which an embodiment may be implemented;

FIG. 26A is an operation flow diagram illustrating an example of anoperation flow of a co-located deployment of a DFS system according tosome embodiments of the present disclosure;

FIG. 26B is an operation flow diagram illustrating an example of anoperation flow of a co-located deployment of a DFS system according tosome embodiments of the present disclosure;

FIG. 26C is a flow diagram illustrative of an embodiment of a routineimplemented by a co-located deployment of a DFS system to activateworker nodes of a distributed computing framework using a search commandaccording to some embodiments of the present disclosure;

FIG. 27 is a cloud based system diagram illustrating a cloud deploymentof a DFS system in which an embodiment may be implemented;

FIG. 28 is a flow diagram illustrating an example of a method performedin a cloud-based DFS system according to some embodiments of the presentdisclosure;

FIG. 29 is a flow diagram illustrating a timeline mechanism thatsupports rendering search results in a time-ordered visualizationaccording to some embodiments of the present disclosure;

FIG. 30 illustrates a timeline visualization rendered on a GUI in whichan embodiment may be implemented;

FIG. 31 illustrates a selected bin of a timeline visualization and thecontents of the selected bin according to some embodiments of thepresent disclosure.

FIG. 32 is a flow diagram illustrating services of a DFS systemaccording to some embodiments of the present disclosure;

FIG. 33 is a system diagram illustrating an environment for ingestingand indexing data, and performing queries on one or more datasets fromone or more dataset sources;

FIG. 34 is a block diagram illustrating an embodiment of multiplemachines, each having multiple nodes;

FIG. 35 is a diagram illustrating an embodiment of a DAG;

FIG. 36 is a block diagram illustrating an embodiment of multiplepartitions being used to implement various search phases of a DAG;

FIG. 37 is a data flow diagram illustrating an embodiment ofcommunications between various components within the environment toprocess and execute a query;

FIG. 38 is a flow diagram illustrative of an embodiment of a routine toprovide query results;

FIG. 39 is a flow diagram illustrative of an embodiment of a routine toprocess a query;

FIG. 40 is a flow diagram illustrative of an embodiment of a routine togenerate a query processing scheme;

FIG. 41 is a flow diagram illustrative of an embodiment of a routine toexecute a query on data from multiple dataset sources;

FIG. 42 is a flow diagram illustrative of an embodiment of a routine toexecute a query on data from an external data source;

FIG. 43 is a flow diagram illustrative of an embodiment of a routine toexecute a query based on a dataset destination;

FIG. 44 is a flow diagram illustrative of an embodiment of a routine toserialize data for communication;

FIG. 45 is a flow diagram illustrative of an embodiment of a routine toexecute a query using a query acceleration data store;

FIG. 46 is a system diagram illustrating an environment for ingestingand indexing data, and performing queries on one or more datasets fromone or more dataset sources including common storage;

FIG. 47 is a flow diagram illustrative of an embodiment of a routine toexecute a query using common storage;

FIG. 48 is a system diagram illustrating an environment for ingestingand indexing data, and performing queries on one or more datasets fromone or more dataset sources including an ingested data buffer;

FIG. 49 is a flow diagram illustrative of an embodiment of a routine toexecute a query using an ingested data buffer;

FIG. 50A is a block diagram of an embodiment of an environment in whicha primary data intake and query system communicates with secondary dataintake and query systems to execute a query;

FIG. 50B is a block diagram of an embodiment of an environment in whicha primary data intake and query system communicates with third-partydata storage and processing systems to execute a query;

FIG. 51 is a data flow diagram illustrating an embodiment ofcommunications between various components described herein to processand execute a federated query;

FIG. 52 is a flow diagram illustrative of an embodiment of a routineimplemented by a query coordinator to execute a query involving datafrom a secondary data intake and query system;

FIGS. 53, 54, 55, and 56 are flow diagrams illustrative of embodimentsof routines implemented by the query coordinator to execute a query ondata from an external data system;

FIG. 57 is a flow diagram illustrative of an embodiment of a routineimplemented by a search head to execute a query received from anexternal data system;

FIG. 58 is a block diagram illustrating an embodiment of a data path ofdata from different data sources in a worker node;

FIG. 59 is a flow diagram illustrative of an embodiment of a routineimplemented by a worker node to process a partition or task;

FIG. 60 is a flow diagram illustrative of an embodiment of a routineimplemented by a query coordinator to optimize and execute a queryinvolving data from an external data system;

FIG. 61 illustrates an example of an external query configuration filein accordance with disclosed embodiments;

FIGS. 62A and 62B are block diagrams illustrating an embodiment of anassignment of bucket data to execution resources based on a bucketdistribution policy;

FIG. 63 is a flow diagram illustrative of an embodiment of a routineimplemented by an indexer to assign bucket data to execution resources;

FIG. 64 is a block diagram illustrating an embodiment of a worker nodeingesting four chunks of data and reducing the records;

FIG. 65 is a flow diagram illustrative of an embodiment of a routineimplemented by a worker node to assign records of chunks of data to oneor more partitions and combine records of the one or more partitions;

FIG. 66 is a flow diagram illustrative of an embodiment of a routineimplemented by a search head to allocate resources and/or estimateexecution time based on records generated during a processing task;

FIG. 67 is a flow diagram illustrative of an embodiment of a routineimplemented by a search head to determine a record generation estimate;

FIG. 68 is a flow diagram illustrative of an embodiment of a routineimplemented by a search head to schedule a query;

FIG. 69 is a flow diagram illustrative of an embodiment of a routineimplemented by a search head to determine a query execution time for aquery;

FIG. 70 is a block diagram illustrating an example of an embodiment inwhich records from multiple chunks of data are used to generate multiplerecords;

FIG. 71 is a flow diagram illustrative of an embodiment of a routineimplemented by a worker node to expand and reduce records from one ormore chunks of data;

FIG. 72 is a block diagram illustrating an example of an embodiment ofthe system assigning a processing task to one or more worker nodes froma search head and/or a query coordinator;

FIG. 73 is a flow diagram illustrative of an embodiment of a routineimplemented by the system to assign a processing task from one componentto one or more different components; and

FIG. 74 is a block diagram illustrating a high-level example of ahardware architecture of a computing system in which an embodiment maybe implemented.

DETAILED DESCRIPTION

Embodiments are described herein according to the following outline:

1.0. GENERAL OVERVIEW 2.0. OVERVIEW OF DATA INTAKE AND QUERY SYSTEMS3.0. GENERAL OVERVIEW 3.1 HOST DEVICES 3.2 CLIENT DEVICES 3.3. CLIENTDEVICE APPLICATIONS 3.4. DATA SERVER SYSTEM 3.5. CLOUD-BASED SYSTEMOVERVIEW 3.6. SEARCHING EXTERNALLY-ARCHIVED DATA 3.7. DATA INGESTION3.7.1. INPUT 3.7.2. PARSING 3.7.3. INDEXING 3.8. QUERY PROCESSING 3.9.PIPELINED SEARCH LANGUAGE 3.10. FIELD EXTRACTION 3.11. EXAMPLE SEARCHSCREEN 3.12. DATA MODELS 3.13. ACCELERATION TECHNIQUE 3.13.1.AGGREGATION TECHNIQUE 3.13.2. KEYWORD INDEX 3.13.3. HIGH PERFORMANCEANALYTICS STORE 3.13.4. EXTRACTING EVENT DATA USING POSTING 3.13.5.ACCELERATING REPORT GENERATION 3.14. SECURITY FEATURES 3.15. DATA CENTERMONITORING 3.16. IT SERVICE MONITORING 4.0. DATA FABRIC SERVICE (DFS)4.1. DFS SYSTEM ARCHITECTURE 4.2. DFS SYSTEM OPERATIONS 5.0. PARALLELEXPORT TECHNIQUES 6.0. DFS QUERY PROCESSING 6.1. ORDERED SEARCH RESULTS6.2. TRANSFORMED SEARCH RESULTS 7.0. CO-LOCATED DEPLOYMENT ARCHITECTURE7.1. CO-LOCATED DEPLOYMENT OPERATIONS 8.0. CLOUD DEPLOYMENT ARCHITECTURE8.1. CLOUD DEPLOYMENT OPERATIONS 9.0. TIMELINE VISUALIZATION 10.0.MONITORING AND METERING SERVICES 11.0. DATA INTAKE AND FABRIC SYSTEMARCHITECTURE 11.1. WORKER NODES 11.1.1. SERIALIZATION/DESERIALIZATION11.2. SEARCH PROCESS MASTER 11.2.1 WORKLOAD CATALOG 11.2.2 NODE MONITOR11.2.3 DATASET COMPENSATION 11.3. QUERY COORDINATOR 11.3.1. QUERYPROCESSING 11.3.2. QUERY EXECUTION AND NODE CONTROL 11.3.3. RESULTPROCESSING 11.4 QUERY ACCELERATION DATA STORE 12.0. QUERY DATA FLOW13.0. QUERY COORDINATOR FLOW 14.0. QUERY PROCESSING FLOW 15.0. WORKLOADMONITORING AND ADVISING FLOW 16.0. MULTIPLE DATASET SOURCES FLOW 17.0.EXTERNAL DATA SOURCE FLOW 18.0. DATASET DESTINATION FLOW 19.0.SERIALIZATION AND DESERIALIZATION FLOW 20.0. ACCELERATED QUERY RESULTSFLOW 21.0. COMMON STORAGE ARCHITECTURE 22.0. COMMON STORAGE FLOW 23.0.INGESTED DATA BUFFER ARCHITECTURE 24.0. INGESTED DATA BUFFER FLOW 25.0.FEDERATED SEARCH 25.1. FEDERATED SEARCH DATA FLOW 26.0. SEARCH OFSECONDARY DATA INTAKE AND QUERY SYSTEM FLOW 27.0. SEARCH WITH DATAINGEST ESTIMATE FLOW 28.0. SEARCH USING SEARCH CONFIGURATION DATA FLOW29.0. DISTRIBUTING PARTIAL RESULTS TO WORKER NODES FLOW 30.0.DISTRIBUTION OF PARTIAL RESULTS BETWEEN WORKER NODES FLOW 31.0.EXECUTING A QUERY RECEIVED FROM ANOTHER SYSTEM FLOW 32.0. TASKDISTRIBUTION WITHIN AN EXECUTION NODE 32.1. WORKER NODE TASKDISTRIBUTION FLOW 33.0 FEDERATED SEARCH OPTIMIZATION 34.0 CONFIGURATIONFILE 35.0. BUCKET DATA DISTRIBUTION FOR PROCESSING/EXPORT 36.0.PARTITIONING AND REDUCING RECORDS DURING INGEST AT A WORKER NODE 37.0.ESTIMATING GENERATED RECORDS 38.0. QUERY-RESOURCE ALLOCATION ANDCONCURRENCY 39.0. SEARCH TIME ESTIMATE 40.0. PROCESSING HIGH CARDINALITYRECORDS WITH RELATED FIELDS 41.0. PUSHING PROCESSING TASKS 42.0.HARDWARE EMBODIMENT 43.0. EXAMPLE EMBODIMENTS 44.0. TERMINOLOGY

In this description, references to “an embodiment,” “one embodiment,” orthe like, mean that the particular feature, function, structure orcharacteristic being described is included in at least one embodiment ofthe technique introduced herein. Occurrences of such phrases in thisspecification do not necessarily all refer to the same embodiment. Onthe other hand, the embodiments referred to are also not necessarilymutually exclusive.

A data intake and query system can index and store data in data storesof indexers, and can receive search queries causing a search of theindexers to obtain search results. The data intake and query systemtypically has search, extraction, execution, and analytics capabilitiesthat may be limited in scope to the data stores of the indexers(“internal data stores”). Hence, a seamless and comprehensive search andanalysis that includes diverse data types from external data sources,common storage (may also be referred to as global data storage or globaldata stores), ingested data buffers, query acceleration data stores,etc. may be difficult. Thus, the capabilities of some data intake andquery systems remain isolated from a variety of data sources that couldimprove search results to provide new insights. Furthermore, theprocessing flow of some data intake and query systems are unidirectionalin that data is obtained from a data source, processed, and thencommunicated to a search head or client without the ability to routedata to different destinations.

The disclosed embodiments overcome these drawbacks by extending thesearch and analytics capabilities of a data intake and query system toinclude diverse data types stored in diverse data systems internal to orexternal from the data intake and query system. As a result, an analystcan use the data intake and query system to search and analyze data froma wide variety of dataset sources, including enterprise systems and opensource technologies of a big data ecosystem. The term “big data” refersto large data sets that may be analyzed computationally to revealpatterns, trends, and associations, in some cases, relating to humanbehavior and interactions.

In particular, introduced herein is a data intake and query system thatthat has the ability to execute big data analytics seamlessly and canscale across diverse data sources to enable processing large volumes ofdiverse data from diverse data systems. A “data source” can include a“data system,” which may refer to a system that can process and/or storedata. A “data storage system” may refer to a storage system that canstore data such as unstructured, semi-structured, or structured data.Accordingly, a data source can include a data system that includes adata storage system.

The system can improve search and analytics capabilities of previoussystems by employing a search process master and query coordinatorscombined with a scalable network of distributed nodes communicativelycoupled to diverse data systems. The network of distributed nodes canact as agents of the data intake and query system to collect and processdata of distributed data systems, and the search process master andcoordinators can provide the processed data to the search head as searchresults.

For example, the data intake and query system can respond to a query byexecuting search operations on various internal and external datasources to obtain partial search results that are harmonized andpresented as search results of the query. As such, the data intake andquery system can offload search and analytics operations to thedistributed nodes. Hence, the system enables search and analyticscapabilities that can extend beyond the data stored on indexers toinclude external data systems, common storage, query acceleration datastores, ingested data buffers, etc.

The system can provide big data open stack integration to act as a bigdata pipeline that extends the search and analytics capabilities of asystem over numerous and diverse data sources. For example, the systemcan extend the data execution scope of the data intake and query systemto include data residing in external data systems such as MySQL,PostgreSQL, and Oracle databases; NoSQL data stores like Cassandra,Mongo DB; cloud storage like Amazon S3 and Hadoop distributed filesystem (HDFS); common storage; ingested data buffers; etc. Thus, thesystem can execute search and analytics operations for all possiblecombinations of data types stored in various data sources.

The distributed processing of the system enables scalability to includeany number of distributed data systems. As such, queries received by thedata intake and query system can be propagated to the network ofdistributed nodes to extend the search and analytics capabilities of thedata intake and query system over different data sources. In thiscontext, the network of distributed nodes can act as an extension of thelocal data intake in query system's data processing pipeline tofacilitate scalable analytics across the diverse data systems.Accordingly, the system can extend and transform the data intake andquery system to include data resources into a data fabric platform thatcan leverage computing assets from anywhere and access and execute ondata regardless of type or origin.

The disclosed embodiments include services such as new searchcapabilities, visualization tools, and other services that areseamlessly integrated into the DFS system. For example, the disclosedtechniques include new search services performed on internal datastores, external data stores, or a combination of both. The searchoperations can provide ordered or unordered search results, or searchresults derived from data of diverse data systems, which can bevisualized to provide new and useful insights about the data containedin a big data ecosystem.

Various other features of the DFS system introduced here will becomeapparent from the description that follows. First, however, it is usefulto consider an example of an environment and system in which thetechniques can be employed, as will now be described.

1.0. General Overview

The embodiments disclosed herein generally refer to an environment thatincludes data intake and query system including a data fabric servicesystem architecture (“DFS system”), services, a network of distributednodes, and distributed data systems, all interconnected over one or morenetworks. However, embodiments of the disclosed environment can includemany computing components including software, servers, routers, clientdevices, and host devices that are not specifically described herein. Asused herein, a “node” can refer to one or more devices and/or softwarerunning on devices that enable the devices to provide execute a task ofthe system. For example, a node can include devices running softwarethat enable the device to execute a portion of a query.

FIG. 1A is a high-level system diagram of an environment 10 in which anembodiment may be implemented. The environment 10 includes distributedexternal data systems 12-1 and 12-2 (also referred to collectively andindividually as external data system(s) 12). The external data systems12 are communicatively coupled (e.g., via a LAN, WAN, etc.) to a dataintake and query system 16, various examples of which are describedherein at least with reference to FIGS. 1A, 2, 3, 4, 18, 25, 27, 33, 46,and 48 . In some embodiments, the external data systems 12 arecommunicatively coupled to worker nodes 14-1 and 14-2 (also referred tocollectively and individually as worker node(s) 14) of the data intakeand query system 16, various examples of which are described herein atleast with reference to FIGS. 18, 25, 27, 33, 46, 48, and 58 . Theenvironment 10 can also include a client device 22 and applicationsrunning on the client device 22. An example includes a personalcomputer, laptop, tablet, phone, or other computing device running anetwork browser application that enables a user of the client device 22to access any of the data systems.

The data intake and query system 16 and the external data systems 12 caneach store data obtained from various data sources. For example, thedata intake and query system 16 can store data in internal data stores20 (also referred to as an internal storage system), and the externaldata systems 12 can store data in respective external data stores 24(also referred to as external storage systems). However, the data intakeand query system 16 and external data systems 12 may process and storedata differently. For example, as explained in greater detail below, thedata intake and query system 16 may store minimally processed orunprocessed data (“raw data”) in the internal data stores 20, which canbe implemented as local data stores 20-1, common storage 20-2, or queryacceleration data stores 20-3. In contrast, the external data systems 12may store pre-processed data rather than raw data. Hence, the dataintake and query system 16 and the external data systems 12 can operateindependent of each other in a big data ecosystem.

The worker nodes 14 can act as agents of the data intake and querysystem 16 to process data collected from the internal data stores 20 andthe external data stores 24. The worker nodes 14 may reside on one ormore computing devices such as servers communicatively coupled to theexternal data systems 12. Other components of the data intake and querysystem 16 can finalize the results before returning the results to theclient device 22. As such, the worker nodes 14 can extend the search andanalytics capabilities of the data intake and query system 16 to act ondiverse data systems.

The external data systems 12 may include one or more computing devicesthat can store structured, semi-structured, or unstructured data. Eachexternal data system 12 can generate and/or collect generated data, andstore the generated data in their respective external data stores 24.For example, the external data system 12-1 may include a server runninga MySQL database that stores structured data objects such astime-stamped events, and the external data system 12-2 may be a serverof cloud computing services such as Amazon web services (AWS) that canprovide different data types ranging from unstructured (e.g., s3) tostructured (e.g., redshift). As yet another non-limiting example, theexternal data system 12-1 and/or 12-2 may be a data intake and querysystem that is separate and distinct from the data intake and querysystem 16, but that includes the same or similar architecture as thedata intake and query system 16 and/or stores data in a similar formatand/or hierarchy. For example, separate divisions of the same companymay set up distinct data intake and query systems 16 that areindependent from each other.

The internal data stores 20 are said to be internal because the datastored thereon has been processed or passed through the data intake andquery system 16 in some form. Conversely, the external data systems 12are said to be external to the data intake and query system 16 becausethe data stored at the external data stores 24 has not necessarily beenprocessed or passed through the data intake and query system 16. Inother words, the data intake and query system 16 may have no control orinfluence over how data is processed, controlled, or managed by theexternal data systems 12, including other instances of a data intake andquery system with the same architecture of the data intake and querysystem 16.

The external data systems 12 can process data, perform requests receivedfrom other computing systems, and perform numerous other computationaltasks independent of each other and independent of the data intake andquery system 16. For example, the external data system 12-1 may be aserver that can process data locally that reflects correlations amongthe stored data. The external data systems 12 may generate and/or storeever increasing volumes of data without any interaction with the dataintake and query system 16. As such, each of the external data system 12may act independently to control, manage, and process the data theycontain.

Data stored in the internal data stores 20 and external data stores 24may be related. For example, an online transaction could generatevarious forms of data stored in disparate locations and in variousformats. The generated data may include payment information, customerinformation, and information about suppliers, retailers, and the like.Other examples of data generated in a big data ecosystem includeapplication program data, system logs, network packet data, error logs,stack traces, and performance data. The data can also include diagnosticinformation and many other types of data that can be analyzed to performlocal actions, diagnose performance problems, monitor interactions, andderive other insights.

The volume of generated data can grow at very high rates as the numberof transactions and diverse data systems grows. A portion of this largevolume of data could be processed and stored by the data intake andquery system 16 while other portions could be stored in any of theexternal data systems 12. In an effort to reduce the vast amounts of rawdata generated in a big data ecosystem, some of the external datasystems 12 may pre-process the raw data based on anticipated dataanalysis needs, store the pre-processed data, discard some or all of theremaining raw data, or store it in a different location that data intakeand query system 16 does not have access to. However, discarding or notmaking the massive amounts of raw data available can result in the lossof valuable insights that could have been obtained by searching all ofthe raw data.

In contrast, the data intake and query system 16 or external datasystems similar to the data intake and query system 16 can address someof these challenges by collecting and storing raw data as structured“events,” as will be described in greater detail below. In someembodiments, an event includes a portion of raw data and is associatedwith a specific point in time. For example, events may be derived from“time series data,” where the time series data comprises a sequence ofdata points (e.g., performance measurements from a computer system) thatare associated with successive points in time.

In some embodiments, the external data systems 12 can store raw data asevents that are indexed by timestamps but are also associated withpredetermined data items. This structure is essentially a modificationof conventional database systems that require predetermining data itemsfor subsequent searches. These systems can be modified to retain theremaining raw data for subsequent re-processing for other predetermineddata items.

Specifically, the raw data can be divided into segments and indexed bytimestamps. The predetermined data items can be associated with theevents indexed by timestamps. The events can be searched only for thepredetermined data items during search time; the events can bere-processed later in time to re-index the raw data, and generate eventswith new predetermined data items. As such, the data systems of thesystem 10 can store related data in a variety of pre-processed data andraw data in a variety of structures.

A number of tools are available to search and analyze data contained inthese diverse data systems. As such, an analyst can use a tool to searcha database of the external data system 12-1. A different tool could beused to search a cloud services application of the external data system12-2. Yet another different tool could be used to search the internaldata stores 20. Moreover, different tools can perform analytics of datastored in proprietary or open source data stores. However, existingtools cannot obtain valuable insights from data contained in acombination of the data intake and query system 16 and/or any of theexternal data systems 12. Examples of these valuable insights mayinclude correlations between the structured data of the external datastores 24 and raw data of the internal data stores 20 (or external datastores 24 that store data in a similar format or hierarchy as theinternal data stores 20).

The disclosed techniques can extend the search, extraction, execution,and analytics capabilities of data intake and query systems toseamlessly search and analyze multiple diverse data of diverse datasystems in a big data ecosystem. The disclosed techniques can transforma big data ecosystem into a big data pipeline between external datasystems and a data intake and query system, to enable seamless searchand analytics operations on a variety of data sources, which can lead tonew insights that were not previously available. Hence, the disclosedtechniques include a data intake and query system 16 extended to searchexternal data systems into a data fabric platform that can leveragecomputing assets from anywhere and access and execute on data regardlessof type and origin. In addition, the data intake and query system 16facilitates implementation of both iterative searches, to read datasetsmultiple times in a loop, and interactive or exploratory data analysis(e.g., for repeated database-style querying of data).

2.0. Overview of Data Intake and Query Systems

As indicated above, modern data centers and other computing environmentscan comprise anywhere from a few host computer systems to thousands ofsystems configured to process data, service requests from remoteclients, and perform numerous other computational tasks. Duringoperation, various components within these computing environments oftengenerate significant volumes of machine data. Machine data is any dataproduced by a machine or component in an information technology (IT)environment and that reflects activity in the IT environment. Forexample, machine data can be raw machine data that is generated byvarious components in IT environments, such as servers, sensors,routers, mobile devices, Internet of Things (IoT) devices, etc. Machinedata can include system logs, network packet data, sensor data,application program data, error logs, stack traces, system performancedata, etc. In general, machine data can also include performance data,diagnostic information, and many other types of data that can beanalyzed to diagnose performance problems, monitor user interactions,and to derive other insights.

A number of tools are available to analyze machine data. In order toreduce the size of the potentially vast amount of machine data that maybe generated, many of these tools typically pre-process the data basedon anticipated data-analysis needs. For example, pre-specified dataitems may be extracted from the machine data and stored in a database tofacilitate efficient retrieval and analysis of those data items atsearch time. However, the rest of the machine data typically is notsaved and is discarded during pre-processing. As storage capacitybecomes progressively cheaper and more plentiful, there are fewerincentives to discard these portions of machine data and many reasons toretain more of the data.

This plentiful storage capacity is presently making it feasible to storemassive quantities of minimally processed machine data for laterretrieval and analysis. In general, storing minimally processed machinedata and performing analysis operations at search time can providegreater flexibility because it enables an analyst to search all of themachine data, instead of searching only a pre-specified set of dataitems. This may enable an analyst to investigate different aspects ofthe machine data that previously were unavailable for analysis.

However, analyzing and searching massive quantities of machine datapresents a number of challenges. For example, a data center, servers, ornetwork appliances may generate many different types and formats ofmachine data (e.g., system logs, network packet data (e.g., wire data,etc.), sensor data, application program data, error logs, stack traces,system performance data, operating system data, virtualization data,etc.) from thousands of different components, which can collectively bevery time-consuming to analyze. In another example, mobile devices maygenerate large amounts of information relating to data accesses,application performance, operating system performance, networkperformance, etc. There can be millions of mobile devices that reportthese types of information.

These challenges can be addressed by using an event-based data intakeand query system, such as the SPLUNK® ENTERPRISE system developed bySplunk Inc. of San Francisco, Calif. The SPLUNK® ENTERPRISE system isthe leading platform for providing real-time operational intelligencethat enables organizations to collect, index, and search machine datafrom various websites, applications, servers, networks, and mobiledevices that power their businesses. The data intake and query system isparticularly useful for analyzing data which is commonly found in systemlog files, network data, and other data input sources. Although many ofthe techniques described herein are explained with reference to a dataintake and query system similar to the SPLUNK® ENTERPRISE system, thesetechniques are also applicable to other types of data systems.

In the data intake and query system, machine data are collected andstored as “events”. An event comprises a portion of machine data and isassociated with a specific point in time. The portion of machine datamay reflect activity in an IT environment and may be produced by acomponent of that IT environment, where the events may be searched toprovide insight into the IT environment, thereby improving theperformance of components in the IT environment. Events may be derivedfrom “time series data,” where the time series data comprises a sequenceof data points (e.g., performance measurements from a computer system,etc.) that are associated with successive points in time. In general,each event has a portion of machine data that is associated with atimestamp that is derived from the portion of machine data in the event.A timestamp of an event may be determined through interpolation betweentemporally proximate events having known timestamps or may be determinedbased on other configurable rules for associating timestamps withevents.

In some instances, machine data can have a predefined format, where dataitems with specific data formats are stored at predefined locations inthe data. For example, the machine data may include data associated withfields in a database table. In other instances, machine data may nothave a predefined format (e.g., may not be at fixed, predefinedlocations), but may have repeatable (e.g., non-random) patterns. Thismeans that some machine data can comprise various data items ofdifferent data types that may be stored at different locations withinthe data. For example, when the data source is an operating system log,an event can include one or more lines from the operating system logcontaining machine data that includes different types of performance anddiagnostic information associated with a specific point in time (e.g., atimestamp).

Examples of components which may generate machine data from which eventscan be derived include, but are not limited to, web servers, applicationservers, databases, firewalls, routers, operating systems, and softwareapplications that execute on computer systems, mobile devices, sensors,Internet of Things (IoT) devices, etc. The machine data generated bysuch data sources can include, for example and without limitation,server log files, activity log files, configuration files, messages,network packet data, performance measurements, sensor measurements, etc.

The data intake and query system uses a flexible schema to specify howto extract information from events. A flexible schema may be developedand redefined as needed. Note that a flexible schema may be applied toevents “on the fly,” when it is needed (e.g., at search time, indextime, ingestion time, etc.). When the schema is not applied to eventsuntil search time, the schema may be referred to as a “late-bindingschema.”

During operation, the data intake and query system receives machine datafrom any type and number of sources (e.g., one or more system logs,streams of network packet data, sensor data, application program data,error logs, stack traces, system performance data, etc.). The systemparses the machine data to produce events each having a portion ofmachine data associated with a timestamp. The system stores the eventsin a data store. The system enables users to run queries against thestored events to, for example, retrieve events that meet criteriaspecified in a query, such as criteria indicating certain keywords orhaving specific values in defined fields. As used herein, the term“field” refers to a location in the machine data of an event containingone or more values for a specific data item. A field may be referencedby a field name associated with the field. As will be described in moredetail herein, a field is defined by an extraction rule (e.g., a regularexpression) that derives one or more values or a sub-portion of textfrom the portion of machine data in each event to produce a value forthe field for that event. The set of values produced aresemantically-related (such as IP address), even though the machine datain each event may be in different formats (e.g., semantically-relatedvalues may be in different positions in the events derived fromdifferent sources).

As described above, the system stores the events in a data store. Theevents stored in the data store are field-searchable, wherefield-searchable herein refers to the ability to search the machine data(e.g., the raw machine data) of an event based on a field specified insearch criteria. For example, a search having criteria that specifies afield name “UserID” may cause the system to field-search the machinedata of events to identify events that have the field name “UserID.” Inanother example, a search having criteria that specifies a field name“UserID” with a corresponding field value “12345” may cause the systemto field-search the machine data of events to identify events havingthat field-value pair (e.g., field name “UserID” with a correspondingfield value of “12345”). Events are field-searchable using one or moreconfiguration files associated with the events. Each configuration fileincludes one or more field names, where each field name is associatedwith a corresponding extraction rule and a set of events to which thatextraction rule applies. The set of events to which an extraction ruleapplies may be identified by metadata associated with the set of events.For example, an extraction rule may apply to a set of events that areeach associated with a particular host, source, or source type. Whenevents are to be searched based on a particular field name specified ina search, the system uses one or more configuration files to determinewhether there is an extraction rule for that particular field name thatapplies to each event that falls within the criteria of the search. Ifso, the event is considered as part of the search results (andadditional processing may be performed on that event based on criteriaspecified in the search). If not, the next event is similarly analyzed,and so on.

As noted above, the data intake and query system utilizes a late-bindingschema while performing queries on events. One aspect of a late-bindingschema is applying extraction rules to events to extract values forspecific fields during search time. More specifically, the extractionrule for a field can include one or more instructions that specify howto extract a value for the field from an event. An extraction rule cangenerally include any type of instruction for extracting values fromevents. In some cases, an extraction rule comprises a regularexpression, where a sequence of characters form a search pattern. Anextraction rule comprising a regular expression is referred to herein asa regex rule. The system applies a regex rule to an event to extractvalues for a field associated with the regex rule, where the values areextracted by searching the event for the sequence of characters definedin the regex rule.

In the data intake and query system, a field extractor may be configuredto automatically generate extraction rules for certain fields in theevents when the events are being created, indexed, or stored, orpossibly at a later time. Alternatively, a user may manually defineextraction rules for fields using a variety of techniques. In contrastto a conventional schema for a database system, a late-binding schema isnot defined at data ingestion time. Instead, the late-binding schema canbe developed on an ongoing basis until the time a query is actuallyexecuted. This means that extraction rules for the fields specified in aquery may be provided in the query itself, or may be located duringexecution of the query. Hence, as a user learns more about the data inthe events, the user can continue to refine the late-binding schema byadding new fields, deleting fields, or modifying the field extractionrules for use the next time the schema is used by the system. Becausethe data intake and query system maintains the underlying machine dataand uses a late-binding schema for searching the machine data, itenables a user to continue investigating and learn valuable insightsabout the machine data.

In some embodiments, a common field name may be used to reference two ormore fields containing equivalent and/or similar data items, even thoughthe fields may be associated with different types of events thatpossibly have different data formats and different extraction rules. Byenabling a common field name to be used to identify equivalent and/orsimilar fields from different types of events generated by disparatedata sources, the system facilitates use of a “common information model”(CIM) across the disparate data sources (further discussed with respectto FIG. 7A).

3.0. General Overview

FIG. 1B is a block diagram of an example networked computer environment100, in accordance with example embodiments. Those skilled in the artwould understand that FIG. 1B represents one example of a networkedcomputer system and other embodiments, such as the embodimentillustrated in FIG. 1A may use different arrangements.

The networked computer environment 100 includes one or more computingdevices. These one or more computing devices comprise any combination ofhardware and software configured to implement the various logicalcomponents described herein. For example, the one or more computingdevices may include one or more memories that store instructions forimplementing the various components described herein, one or morehardware processors configured to execute the instructions stored in theone or more memories, and various data repositories in the one or morememories for storing data structures utilized and manipulated by thevarious components.

In some embodiments, one or more client devices 102 are coupled to oneor more host devices 106 and a data intake and query system 108 via oneor more networks 104. Networks 104 broadly represent one or more LANs,WANs, cellular networks (e.g., LTE, HSPA, 3G, and other cellulartechnologies), and/or networks using any of wired, wireless, terrestrialmicrowave, or satellite links, and may include the public Internet.

3.1 Host Devices

In the illustrated embodiment, an environment 100 includes one or morehost devices 106. Host devices 106 may broadly include any number ofcomputers, virtual machine instances, and/or data centers that areconfigured to host or execute one or more instances of host applications114. In general, a host device 106 may be involved, directly orindirectly, in processing requests received from client devices 102.Each host device 106 may comprise, for example, one or more of a networkdevice, a web server, an application server, a database server, etc. Acollection of host devices 106 may be configured to implement anetwork-based service. For example, a provider of a network-basedservice may configure one or more host devices 106 and host applications114 (e.g., one or more web servers, application servers, databaseservers, etc.) to collectively implement the network-based application.

In general, client devices 102 communicate with one or more hostapplications 114 to exchange information. The communication between aclient device 102 and a host application 114 may, for example, be basedon the Hypertext Transfer Protocol (HTTP) or any other network protocol.Content delivered from the host application 114 to a client device 102may include, for example, HTML documents, media content, etc. Thecommunication between a client device 102 and host application 114 mayinclude sending various requests and receiving data packets. Forexample, in general, a client device 102 or application running on aclient device may initiate communication with a host application 114 bymaking a request for a specific resource (e.g., based on an HTTPrequest), and the application server may respond with the requestedcontent stored in one or more response packets.

In the illustrated embodiment, one or more of host applications 114 maygenerate various types of performance data during operation, includingevent logs, network data, sensor data, and other types of machine data.For example, a host application 114 comprising a web server may generateone or more web server logs in which details of interactions between theweb server and any number of client devices 102 is recorded. As anotherexample, a host device 106 comprising a router may generate one or morerouter logs that record information related to network traffic managedby the router. As yet another example, a host application 114 comprisinga database server may generate one or more logs that record informationrelated to requests sent from other host applications 114 (e.g., webservers or application servers) for data managed by the database server.

3.2 Client Devices

Client devices 102 represent any computing device capable of interactingwith one or more host devices 106 via a network 104. Examples of clientdevices 102 may include, without limitation, smart phones, tabletcomputers, handheld computers, wearable devices, laptop computers,desktop computers, servers, portable media players, gaming devices, andso forth. In general, a client device 102 can provide access todifferent content, for instance, content provided by one or more hostdevices 106, etc. Each client device 102 may comprise one or more clientapplications 110, described in more detail in a separate sectionhereinafter.

3.3. Client Device Applications

In some embodiments, each client device 102 may host or execute one ormore client applications 110 that are capable of interacting with one ormore host devices 106 via one or more networks 104. For instance, aclient application 110 may be or comprise a web browser that a user mayuse to navigate to one or more websites or other resources provided byone or more host devices 106. As another example, a client application110 may comprise a mobile application or “app.” For example, an operatorof a network-based service hosted by one or more host devices 106 maymake available one or more mobile apps that enable users of clientdevices 102 to access various resources of the network-based service. Asyet another example, client applications 110 may include backgroundprocesses that perform various operations without direct interactionfrom a user. A client application 110 may include a “plug-in” or“extension” to another application, such as a web browser plug-in orextension.

In some embodiments, a client application 110 may include a monitoringcomponent 112. At a high level, the monitoring component 112 comprises asoftware component or other logic that facilitates generatingperformance data related to a client device's operating state, includingmonitoring network traffic sent and received from the client device andcollecting other device and/or application-specific information.Monitoring component 112 may be an integrated component of a clientapplication 110, a plug-in, an extension, or any other type of add-oncomponent. Monitoring component 112 may also be a stand-alone process.

In some embodiments, a monitoring component 112 may be created when aclient application 110 is developed, for example, by an applicationdeveloper using a software development kit (SDK). The SDK may includecustom monitoring code that can be incorporated into the codeimplementing a client application 110. When the code is converted to anexecutable application, the custom code implementing the monitoringfunctionality can become part of the application itself.

In some embodiments, an SDK or other code for implementing themonitoring functionality may be offered by a provider of a data intakeand query system, such as a system 108. In such cases, the provider ofthe system 108 can implement the custom code so that performance datagenerated by the monitoring functionality is sent to the system 108 tofacilitate analysis of the performance data by a developer of the clientapplication or other users.

In some embodiments, the custom monitoring code may be incorporated intothe code of a client application 110 in a number of different ways, suchas the insertion of one or more lines in the client application codethat call or otherwise invoke the monitoring component 112. As such, adeveloper of a client application 110 can add one or more lines of codeinto the client application 110 to trigger the monitoring component 112at desired points during execution of the application. Code thattriggers the monitoring component may be referred to as a monitortrigger. For instance, a monitor trigger may be included at or near thebeginning of the executable code of the client application 110 such thatthe monitoring component 112 is initiated or triggered as theapplication is launched, or included at other points in the code thatcorrespond to various actions of the client application, such as sendinga network request or displaying a particular interface.

In some embodiments, the monitoring component 112 may monitor one ormore aspects of network traffic sent and/or received by a clientapplication 110. For example, the monitoring component 112 may beconfigured to monitor data packets transmitted to and/or from one ormore host applications 114. Incoming and/or outgoing data packets can beread or examined to identify network data contained within the packets,for example, and other aspects of data packets can be analyzed todetermine a number of network performance statistics. Monitoring networktraffic may enable information to be gathered particular to the networkperformance associated with a client application 110 or set ofapplications.

In some embodiments, network performance data refers to any type of datathat indicates information about the network and/or network performance.Network performance data may include, for instance, a URL requested, aconnection type (e.g., HTTP, HTTPS, etc.), a connection start time, aconnection end time, an HTTP status code, request length, responselength, request headers, response headers, connection status (e.g.,completion, response time(s), failure, etc.), and the like. Uponobtaining network performance data indicating performance of thenetwork, the network performance data can be transmitted to a dataintake and query system 108 for analysis.

Upon developing a client application 110 that incorporates a monitoringcomponent 112, the client application 110 can be distributed to clientdevices 102. Applications generally can be distributed to client devices102 in any manner, or they can be pre-loaded. In some cases, theapplication may be distributed to a client device 102 via an applicationmarketplace or other application distribution system. For instance, anapplication marketplace or other application distribution system mightdistribute the application to a client device based on a request fromthe client device to download the application.

Examples of functionality that enables monitoring performance of aclient device are described in U.S. patent application Ser. No.14/524,748, entitled “UTILIZING PACKET HEADERS TO MONITOR NETWORKTRAFFIC IN ASSOCIATION WITH A CLIENT DEVICE”, filed on 27 Oct. 2014, andwhich is hereby incorporated by reference in its entirety for allpurposes.

In some embodiments, the monitoring component 112 may also monitor andcollect performance data related to one or more aspects of theoperational state of a client application 110 and/or client device 102.For example, a monitoring component 112 may be configured to collectdevice performance information by monitoring one or more client deviceoperations, or by making calls to an operating system and/or one or moreother applications executing on a client device 102 for performanceinformation. Device performance information may include, for instance, acurrent wireless signal strength of the device, a current connectiontype and network carrier, current memory performance information, ageographic location of the device, a device orientation, and any otherinformation related to the operational state of the client device.

In some embodiments, the monitoring component 112 may also monitor andcollect other device profile information including, for example, a typeof client device, a manufacturer, and model of the device, versions ofvarious software applications installed on the device, and so forth.

In general, a monitoring component 112 may be configured to generateperformance data in response to a monitor trigger in the code of aclient application 110 or other triggering application event, asdescribed above, and to store the performance data in one or more datarecords. Each data record, for example, may include a collection offield-value pairs, each field-value pair storing a particular item ofperformance data in association with a field for the item. For example,a data record generated by a monitoring component 112 may include a“networkLatency” field (not shown in the Figure) in which a value isstored. This field indicates a network latency measurement associatedwith one or more network requests. The data record may include a “state”field to store a value indicating a state of a network connection, andso forth for any number of aspects of collected performance data.

3.4. Data Server System

FIG. 2 is a block diagram of an example data intake and query system108, in accordance with example embodiments. In certain embodiments, thedata intake and query system 108 may be or may include a data intake andquery system 16. System 108 includes one or more forwarders 204 thatreceive data from a variety of input data sources 203, and one or moreindexers 206 that process and store the data in one or more data stores208. These forwarders 204 and indexers 206 can comprise separatecomputer systems, or may alternatively comprise separate processesexecuting on one or more computer systems.

Each data source 203 broadly represents a distinct source of data thatcan be consumed by system 108. Examples of a data sources 203 include,without limitation, data files, directories of files, data sent over anetwork, event logs, registries, etc.

During operation, the forwarders 204 identify which indexers 206 receivedata collected from a data source 203 and forward the data to theappropriate indexers. Forwarders 204 can also perform operations on thedata before forwarding, including removing extraneous data, detectingtimestamps in the data, parsing data, indexing data, routing data basedon criteria relating to the data being routed, and/or performing otherdata transformations.

In some embodiments, a forwarder 204 may comprise a service accessibleto client devices 102 and host devices 106 via a network 104. Forexample, one type of forwarder 204 may be capable of consuming vastamounts of real-time data from a potentially large number of clientdevices 102 and/or host devices 106. The forwarder 204 may, for example,comprise a computing device which implements multiple data pipelines or“queues” to handle forwarding of network data to indexers 206. Aforwarder 204 may also perform many of the functions that are performedby an indexer. For example, a forwarder 204 may perform keywordextractions on raw data or parse raw data to create events. A forwarder204 may generate time stamps for events. Additionally or alternatively,a forwarder 204 may perform routing of events to indexers 206. Datastore 208 may contain events derived from machine data from a variety ofsources all pertaining to the same component in an IT environment, andthis data may be produced by the machine in question or by othercomponents in the IT environment.

3.5. Cloud-Based System Overview

The example data intake and query system 108 described in reference toFIG. 2 comprises several system components, including one or moreforwarders, indexers, and search heads. In some environments, a user ofa data intake and query system 108 may install and configure, oncomputing devices owned and operated by the user, one or more softwareapplications that implement some or all of these system components. Forexample, a user may install a software application on server computersowned by the user and configure each server to operate as one or more ofa forwarder, an indexer, a search head, etc. This arrangement generallymay be referred to as an “on-premises” solution. That is, the system 108is installed and operates on computing devices directly controlled bythe user of the system. Some users may prefer an on-premises solutionbecause it may provide a greater level of control over the configurationof certain aspects of the system (e.g., security, privacy, standards,controls, etc.). However, other users may instead prefer an arrangementin which the user is not directly responsible for providing and managingthe computing devices upon which various components of system 108operate.

In one embodiment, to provide an alternative to an entirely on-premisesenvironment for system 108, one or more of the components of a dataintake and query system instead may be provided as a cloud-basedservice. In this context, a cloud-based service refers to a servicehosted by one more computing resources that are accessible to end usersover a network, for example, by using a web browser or other applicationon a client device to interface with the remote computing resources. Forexample, a service provider may provide a cloud-based data intake andquery system by managing computing resources configured to implementvarious aspects of the system (e.g., forwarders, indexers, search heads,etc.) and by providing access to the system to end users via a network.Typically, a user may pay a subscription or other fee to use such aservice. Each subscribing user of the cloud-based service may beprovided with an account that enables the user to configure a customizedcloud-based system based on the user's preferences.

FIG. 3 illustrates a block diagram of an example cloud-based data intakeand query system 306. Similar to the system of FIG. 2 , the networkedcomputer environment 300 includes input data sources 203 and forwarders204. These input data sources and forwarders may be in a subscriber'sprivate computing environment. Alternatively, they might be directlymanaged by the service provider as part of the cloud service. In theexample environment 300, one or more forwarders 204 and client devices302 are coupled to a cloud-based data intake and query system 306 viaone or more networks 304. Network 304 broadly represents one or moreLANs, WANs, cellular networks, intranetworks, internetworks, etc., usingany of wired, wireless, terrestrial microwave, satellite links, etc.,and may include the public Internet, and is used by client devices 302and forwarders 204 to access the system 306. Similar to the system of38, each of the forwarders 204 may be configured to receive data from aninput source and to forward the data to other components of the system306 for further processing.

In some embodiments, a cloud-based data intake and query system 306 maycomprise a plurality of system instances 308. In general, each systeminstance 308 may include one or more computing resources managed by aprovider of the cloud-based system 306 made available to a particularsubscriber. The computing resources comprising a system instance 308may, for example, include one or more servers or other devicesconfigured to implement one or more forwarders, indexers, search heads,and other components of a data intake and query system, similar tosystem 108. As indicated above, a subscriber may use a web browser orother application of a client device 302 to access a web portal or otherinterface that enables the subscriber to configure an instance 308.

Providing a data intake and query system as described in reference tosystem 108 as a cloud-based service presents a number of challenges.Each of the components of a system 108 (e.g., forwarders, indexers, andsearch heads) may at times refer to various configuration files storedlocally at each component. These configuration files typically mayinvolve some level of user configuration to accommodate particular typesof data a user desires to analyze and to account for other userpreferences. However, in a cloud-based service context, users typicallymay not have direct access to the underlying computing resourcesimplementing the various system components (e.g., the computingresources comprising each system instance 308) and may desire to makesuch configurations indirectly, for example, using one or more web-basedinterfaces. Thus, the techniques and systems described herein forproviding user interfaces that enable a user to configure source typedefinitions are applicable to both on-premises and cloud-based servicecontexts, or some combination thereof (e.g., a hybrid system where bothan on-premises environment, such as SPLUNK® ENTERPRISE, and acloud-based environment, such as SPLUNK CLOUD™, are centrally visible).

3.6. Searching Externally-Archived Data

FIG. 4 shows a block diagram of an example of a data intake and querysystem 108 that provides transparent search facilities for data systemsthat are external to the data intake and query system. Such facilitiesare available in the Splunk® Analytics for Hadoop® system provided bySplunk Inc. of San Francisco, Calif. Splunk® Analytics for Hadoop®represents an analytics platform that enables business and IT teams torapidly explore, analyze, and visualize data in Hadoop® and NoSQL datastores.

The search head 210 of the data intake and query system receives searchrequests from one or more client devices 404 over network connections420. As discussed above, the data intake and query system 108 may residein an enterprise location, in the cloud, etc. FIG. 4 illustrates thatmultiple client devices 404 a, 404 b . . . 404 n may communicate withthe data intake and query system 108. The client devices 404 maycommunicate with the data intake and query system using a variety ofconnections. For example, one client device in FIG. 4 is illustrated ascommunicating over an Internet (Web) protocol, another client device isillustrated as communicating via a command line interface, and anotherclient device is illustrated as communicating via a software developerkit (SDK).

The search head 210 analyzes the received search request to identifyrequest parameters. If a search request received from one of the clientdevices 404 references an index maintained by the data intake and querysystem, then the search head 210 connects to one or more indexers 206 ofthe data intake and query system for the index referenced in the requestparameters. That is, if the request parameters of the search requestreference an index, then the search head accesses the data in the indexvia the indexer. The data intake and query system 108 may include one ormore indexers 206, depending on system access resources andrequirements. As described further below, the indexers 206 retrieve datafrom their respective local data stores 208 as specified in the searchrequest. The indexers and their respective data stores can comprise oneor more storage devices and typically reside on the same system, thoughthey may be connected via a local network connection.

If the request parameters of the received search request reference anexternal data collection, which is not accessible to the indexers 206 orunder the management of the data intake and query system, then thesearch head 210 can access the external data collection through anExternal Result Provider (ERP) process 410. An external data collectionmay be referred to as a “virtual index” (plural, “virtual indices”). AnERP process provides an interface through which the search head 210 mayaccess virtual indices.

Thus, a search reference to an index of the system relates to a locallystored and managed data collection. In contrast, a search reference to avirtual index relates to an externally stored and managed datacollection, which the search head may access through one or more ERPprocesses 410, 412. FIG. 4 shows two ERP processes 410, 412 that connectto respective remote (external) virtual indices, which are indicated asa Hadoop or another system 414 (e.g., Amazon S3, Amazon EMR, otherHadoop® Compatible File Systems (HCFS), etc.) and arelational databasemanagement system (RDBMS) 416. Other virtual indices may include otherfile organizations and protocols, such as Structured Query Language(SQL) and the like. The ellipses between the ERP processes 410, 412indicate optional additional ERP processes of the data intake and querysystem 108. An ERP process may be a computer process that is initiatedor spawned by the search head 210 and is executed by the search dataintake and query system 108. Alternatively or additionally, an ERPprocess may be a process spawned by the search head 210 on the same ordifferent host system as the search head 210 resides.

The search head 210 may spawn a single ERP process in response tomultiple virtual indices referenced in a search request, or the searchhead may spawn different ERP processes for different virtual indices.Generally, virtual indices that share common data configurations orprotocols may share ERP processes. For example, all search queryreferences to a Hadoop file system may be processed by the same ERPprocess, if the ERP process is suitably configured. Likewise, all searchquery references to a SQL database may be processed by the same ERPprocess. In addition, the search head may provide a common ERP processfor common external data source types (e.g., a common vendor may utilizea common ERP process, even if the vendor includes different data storagesystem types, such as Hadoop and SQL). Common indexing schemes also maybe handled by common ERP processes, such as flat text files or Weblogfiles.

The search head 210 determines the number of ERP processes to beinitiated via the use of configuration parameters that are included in asearch request message. Generally, there is a one-to-many relationshipbetween an external results provider “family” and ERP processes. Thereis also a one-to-many relationship between an ERP process andcorresponding virtual indices that are referred to in a search request.For example, using RDBMS, assume two independent instances of such asystem by one vendor, such as one RDBMS for production and another RDBMSused for development. In such a situation, it is likely preferable (butoptional) to use two ERP processes to maintain the independent operationas between production and development data. Both of the ERPs, however,will belong to the same family, because the two RDBMS system types arefrom the same vendor.

The ERP processes 410, 412 receive a search request from the search head210. The search head may optimize the received search request forexecution at the respective external virtual index. Alternatively, theERP process may receive a search request as a result of analysisperformed by the search head or by a different system process. The ERPprocesses 410, 412 can communicate with the search head 210 viaconventional input/output routines (e.g., standard in/standard out,etc.). In this way, the ERP process receives the search request from aclient device such that the search request may be efficiently executedat the corresponding external virtual index.

The ERP processes 410, 412 may be implemented as a process of the dataintake and query system. Each ERP process may be provided by the dataintake and query system, or may be provided by process or applicationproviders who are independent of the data intake and query system. Eachrespective ERP process may include an interface application installed ata computer of the external result provider that ensures propercommunication between the search support system and the external resultprovider. The ERP processes 410, 412 generate appropriate searchrequests in the protocol and syntax of the respective virtual indices414, 416, each of which corresponds to the search request received bythe search head 210. Upon receiving search results from theircorresponding virtual indices, the respective ERP process passes theresult to the search head 210, which may return or display the resultsor a processed set of results based on the returned results to therespective client device.

Client devices 404 may communicate with the data intake and query system108 through a network interface 420, e.g., one or more LANs, WANs,cellular networks, intranetworks, and/or internetworks using any ofwired, wireless, terrestrial microwave, satellite links, etc., and mayinclude the public Internet.

The analytics platform utilizing the External Result Provider processdescribed in more detail in U.S. Pat. No. 8,738,629, entitled “EXTERNALRESULT PROVIDED PROCESS FOR RETRIEVING DATA STORED USING A DIFFERENTCONFIGURATION OR PROTOCOL”, issued on 27 May 2014, U.S. Pat. No.8,738,587, entitled “PROCESSING A SYSTEM SEARCH REQUEST BY RETRIEVINGRESULTS FROM BOTH A NATIVE INDEX AND A VIRTUAL INDEX”, issued on 25 Jul.2013, U.S. patent application Ser. No. 14/266,832, entitled “PROCESSINGA SYSTEM SEARCH REQUEST ACROSS DISPARATE DATA COLLECTION SYSTEMS”, filedon 1 May 2014, and U.S. Pat. No. 9,514,189, entitled “PROCESSING ASYSTEM SEARCH REQUEST INCLUDING EXTERNAL DATA SOURCES”, issued on 6 Dec.2016, each of which is hereby incorporated by reference in its entiretyfor all purposes.

3.6.1. ERP Process Features

The ERP processes described above may include two operation modes: astreaming mode and a reporting mode. The ERP processes can operate instreaming mode only, in reporting mode only, or in both modessimultaneously. Operating in both modes simultaneously is referred to asmixed mode operation. In a mixed mode operation, the ERP at some pointcan stop providing the search head with streaming results and onlyprovide reporting results thereafter, or the search head at some pointmay start ignoring streaming results it has been using and only usereporting results thereafter.

The streaming mode returns search results in real time, with minimalprocessing, in response to the search request. The reporting modeprovides results of a search request with processing of the searchresults prior to providing them to the requesting search head, which inturn provides results to the requesting client device. ERP operationwith such multiple modes provides greater performance flexibility withregard to report time, search latency, and resource utilization.

In a mixed mode operation, both streaming mode and reporting mode areoperating simultaneously. The streaming mode results (e.g., the machinedata obtained from the external data source) are provided to the searchhead, which can then process the results data (e.g., break the machinedata into events, timestamp it, filter it, etc.) and integrate theresults data with the results data from other external data sources,and/or from data stores of the search head. The search head performssuch processing and can immediately start returning interim (streamingmode) results to the user at the requesting client device;simultaneously, the search head is waiting for the ERP process toprocess the data it is retrieving from the external data source as aresult of the concurrently executing reporting mode.

In some instances, the ERP process initially operates in a mixed mode,such that the streaming mode operates to enable the ERP quickly toreturn interim results (e.g., some of the machined data or unprocesseddata necessary to respond to a search request) to the search head,enabling the search head to process the interim results and beginproviding to the client or search requester interim results that areresponsive to the query. Meanwhile, in this mixed mode, the ERP alsooperates concurrently in reporting mode, processing portions of machinedata in a manner responsive to the search query. Upon determining thatit has results from the reporting mode available to return to the searchhead, the ERP may halt processing in the mixed mode at that time (orsome later time) by stopping the return of data in streaming mode to thesearch head and switching to reporting mode only. The ERP at this pointstarts sending interim results in reporting mode to the search head,which in turn may then present this processed data responsive to thesearch request to the client or search requester. Typically the searchhead switches from using results from the ERP's streaming mode ofoperation to results from the ERP's reporting mode of operation when thehigher bandwidth results from the reporting mode outstrip the amount ofdata processed by the search head in the streaming mode of ERPoperation.

A reporting mode may have a higher bandwidth because the ERP does nothave to spend time transferring data to the search head for processingall the machine data. In addition, the ERP may optionally direct anotherprocessor to do the processing.

The streaming mode of operation does not need to be stopped to gain thehigher bandwidth benefits of a reporting mode; the search head couldsimply stop using the streaming mode results—and start using thereporting mode results—when the bandwidth of the reporting mode hascaught up with or exceeded the amount of bandwidth provided by thestreaming mode. Thus, it will be understood that a variety of triggersand ways to accomplish a search head's switch from using streaming moderesults to using reporting mode results may be used.

The reporting mode can involve the ERP process (or an external system)performing event breaking, time stamping, filtering of events to matchthe search query request, and calculating statistics on the results. Theuser can request particular types of data, such as if the search queryitself involves types of events, or the search request may ask forstatistics on data, such as on events that meet the search request. Ineither case, the search head understands the query language used in thereceived query request, which may be a proprietary language. Oneexemplary query language is Splunk Processing Language (SPL) developedby the assignee of the application, Splunk Inc. The search headtypically understands how to use that language to obtain data from theindexers, which store data in a format used by the SPLUNK® Enterprisesystem.

The ERP processes support the search head, as the search head is notordinarily configured to understand the format in which data is storedin external data sources such as Hadoop or SQL data systems. Rather, theERP process performs that translation from the query submitted in thesearch support system's native format (e.g., SPL if SPLUNK® ENTERPRISEis used as the search support system) to a search query request formatthat will be accepted by the corresponding external data system. Theexternal data system typically stores data in a different format fromthat of the search support system's native index format, and it utilizesa different query language (e.g., SQL or MapReduce, rather than SPL orthe like).

As noted, the ERP process can operate in the streaming mode alone. Afterthe ERP process has performed the translation of the query request andreceived raw results from the streaming mode, the search head canintegrate the returned data with any data obtained from local datasources (e.g., native to the search support system), other external datasources, and other ERP processes (if such operations were required tosatisfy the terms of the search query). An advantage of mixed modeoperation is that, in addition to streaming mode, the ERP process isalso executing concurrently in reporting mode. Thus, the ERP process(rather than the search head) is processing query results (e.g.,performing event breaking, timestamping, filtering, possibly calculatingstatistics if required to be responsive to the search query request,etc.). It should be apparent to those skilled in the art that additionaltime is needed for the ERP process to perform the processing in such aconfiguration. Therefore, the streaming mode will allow the search headto start returning interim results to the user at the client devicebefore the ERP process can complete sufficient processing to startreturning any search results. The switchover between streaming andreporting mode happens when the ERP process determines that theswitchover is appropriate, such as when the ERP process determines itcan begin returning meaningful results from its reporting mode.

The operation described above illustrates the source of operationallatency: streaming mode has low latency (immediate results) and usuallyhas relatively low bandwidth (fewer results can be returned per unit oftime). In contrast, the concurrently running reporting mode hasrelatively high latency (it has to perform a lot more processing beforereturning any results) and usually has relatively high bandwidth (moreresults can be processed per unit of time). For example, when the ERPprocess does begin returning report results, it returns more processedresults than in the streaming mode, because, e.g., statistics only needto be calculated to be responsive to the search request. That is, theERP process doesn't have to take time to first return machine data tothe search head. As noted, the ERP process could be configured tooperate in streaming mode alone and return just the machine data for thesearch head to process in a way that is responsive to the searchrequest. Alternatively, the ERP process can be configured to operate inthe reporting mode only. Also, the ERP process can be configured tooperate in streaming mode and reporting mode concurrently, as described,with the ERP process stopping the transmission of streaming results tothe search head when the concurrently running reporting mode has caughtup and started providing results. The reporting mode does not requirethe processing of all machine data that is responsive to the searchquery request before the ERP process starts returning results; rather,the reporting mode usually performs processing of chunks of events andreturns the processing results to the search head for each chunk.

For example, an ERP process can be configured to merely return thecontents of a search result file verbatim, with little or no processingof results. That way, the search head performs all processing (such asparsing byte streams into events, filtering, etc.). The ERP process canbe configured to perform additional intelligence, such as analyzing thesearch request and handling all the computation that a native searchindexer process would otherwise perform. In this way, the configured ERPprocess provides greater flexibility in features while operatingaccording to desired preferences, such as response latency and resourcerequirements.

3.7. Data Ingestion

FIG. 5A is a flow chart of an example method that illustrates howindexers process, index, and store data received from forwarders, inaccordance with example embodiments. The data flow illustrated in FIG.5A is provided for illustrative purposes only; those skilled in the artwould understand that one or more of the steps of the processesillustrated in FIG. 5A may be removed or that the ordering of the stepsmay be changed. Furthermore, for the purposes of illustrating a clearexample, one or more particular system components are described in thecontext of performing various operations during each of the data flowstages. For example, a forwarder is described as receiving andprocessing machine data during an input phase; an indexer is describedas parsing and indexing machine data during parsing and indexing phases;and a search head is described as performing a search query during asearch phase. However, other system arrangements and distributions ofthe processing steps across system components may be used.

3.7.1. Input

At block 502, a forwarder receives data from an input source, such as adata source 203 shown in FIG. 2 . A forwarder initially may receive thedata as a raw data stream generated by the input source. For example, aforwarder may receive a data stream from a log file generated by anapplication server, from a stream of network data from a network device,or from any other source of data. In some embodiments, a forwarderreceives the raw data and may segment the data stream into “blocks”,possibly of a uniform data size, to facilitate subsequent processingsteps.

At block 504, a forwarder or other system component annotates each blockgenerated from the raw data with one or more metadata fields. Thesemetadata fields may, for example, provide information related to thedata block as a whole and may apply to each event that is subsequentlyderived from the data in the data block. For example, the metadatafields may include separate fields specifying each of a host, a source,and a source type related to the data block. A host field may contain avalue identifying a host name or IP address of a device that generatedthe data. A source field may contain a value identifying a source of thedata, such as a pathname of a file or a protocol and port related toreceived network data. A source type field may contain a valuespecifying a particular source type label for the data. Additionalmetadata fields may also be included during the input phase, such as acharacter encoding of the data, if known, and possibly other values thatprovide information relevant to later processing steps. In someembodiments, a forwarder forwards the annotated data blocks to anothersystem component (typically an indexer) for further processing.

The data intake and query system allows forwarding of data from one dataintake and query instance to another, or even to a third-party system.The data intake and query system can employ different types offorwarders in a configuration.

In some embodiments, a forwarder may contain the essential componentsneeded to forward data. A forwarder can gather data from a variety ofinputs and forward the data to an indexer for indexing and searching. Aforwarder can also tag metadata (e.g., source, source type, host, etc.).

In some embodiments, a forwarder has the capabilities of theaforementioned forwarder as well as additional capabilities. Theforwarder can parse data before forwarding the data (e.g., can associatea time stamp with a portion of data and create an event, etc.) and canroute data based on criteria such as source or type of event. Theforwarder can also index data locally while forwarding the data toanother indexer.

3.7.2. Parsing

At block 506, an indexer receives data blocks from a forwarder andparses the data to organize the data into events. In some embodiments,to organize the data into events, an indexer may determine a source typeassociated with each data block (e.g., by extracting a source type labelfrom the metadata fields associated with the data block, etc.) and referto a source type configuration corresponding to the identified sourcetype. The source type definition may include one or more properties thatindicate to the indexer to automatically determine the boundaries withinthe received data that indicate the portions of machine data for events.In general, these properties may include regular expression-based rulesor delimiter rules where, for example, event boundaries may be indicatedby predefined characters or character strings. These predefinedcharacters may include punctuation marks or other special charactersincluding, for example, carriage returns, tabs, spaces, line breaks,etc. If a source type for the data is unknown to the indexer, an indexermay infer a source type for the data by examining the structure of thedata. Then, the indexer can apply an inferred source type definition tothe data to create the events.

At block 508, the indexer determines a timestamp for each event. Similarto the process for parsing machine data, an indexer may again refer to asource type definition associated with the data to locate one or moreproperties that indicate instructions for determining a timestamp foreach event. The properties may, for example, instruct an indexer toextract a time value from a portion of data for the event, tointerpolate time values based on timestamps associated with temporallyproximate events, to create a timestamp based on a time the portion ofmachine data was received or generated, to use the timestamp of aprevious event, or use any other rules for determining timestamps.

At block 510, the indexer associates with each event one or moremetadata fields including a field containing the timestamp determinedfor the event. In some embodiments, a timestamp may be included in themetadata fields. These metadata fields may include any number of“default fields” that are associated with all events, and may alsoinclude one more custom fields as defined by a user. Similar to themetadata fields associated with the data blocks at block 504, thedefault metadata fields associated with each event may include a host,source, and source type field including or in addition to a fieldstoring the timestamp.

At block 512, an indexer may optionally apply one or moretransformations to data included in the events created at block 506. Forexample, such transformations can include removing a portion of an event(e.g., a portion used to define event boundaries, extraneous charactersfrom the event, other extraneous text, etc.), masking a portion of anevent (e.g., masking a credit card number), removing redundant portionsof an event, etc. The transformations applied to events may, forexample, be specified in one or more configuration files and referencedby one or more source type definitions.

FIG. 5C illustrates an illustrative example of machine data can bestored in a data store in accordance with various disclosed embodiments.In other embodiments, machine data can be stored in a flat file in acorresponding bucket with an associated index file, such as a timeseries index or “TSIDX.” As such, the depiction of machine data andassociated metadata as rows and columns in the table of FIG. 5C ismerely illustrative and is not intended to limit the data format inwhich the machine data and metadata is stored in various embodimentsdescribed herein. In one particular embodiment, machine data can bestored in a compressed or encrypted formatted. In such embodiments, themachine data can be stored with or be associated with data thatdescribes the compression or encryption scheme with which the machinedata is stored. The information about the compression or encryptionscheme can be used to decompress or decrypt the machine data, and anymetadata with which it is stored, at search time.

As mentioned above, certain metadata, e.g., host 536, source 537, sourcetype 538, and timestamps 535 can be generated for each event, andassociated with a corresponding portion of machine data 539 when storingthe event data in a data store, e.g., data store 208. Any of themetadata can be extracted from the corresponding machine data, orsupplied or defined by an entity, such as a user or computer system. Themetadata fields can become part of or stored with the event. Note thatwhile the time-stamp metadata field can be extracted from the raw dataof each event, the values for the other metadata fields may bedetermined by the indexer based on information it receives pertaining tothe source of the data separate from the machine data.

While certain default or user-defined metadata fields can be extractedfrom the machine data for indexing purposes, all the machine data withinan event can be maintained in its original condition. As such, inembodiments in which the portion of machine data included in an event isunprocessed or otherwise unaltered, it is referred to herein as aportion of raw machine data. In other embodiments, the port of machinedata in an event can be processed or otherwise altered. As such, unlesscertain information needs to be removed for some reasons (e.g.extraneous information, confidential information), all the raw machinedata contained in an event can be preserved and saved in its originalform. Accordingly, the data store in which the event records are storedis sometimes referred to as a “raw record data store.” The raw recorddata store contains a record of the raw event data tagged with thevarious default fields.

In FIG. 5C, the first three rows of the table represent events 531, 532,and 533 and are related to a server access log that records requestsfrom multiple clients processed by a server, as indicated by entry of“access.log” in the source column 537.

In the example shown in FIG. 5C, each of the events 531-534 isassociated with a discrete request made from a client device. The rawmachine data generated by the server and extracted from a server accesslog can include the IP address of the client 540, the user id of theperson requesting the document 541, the time the server finishedprocessing the request 542, the request line from the client 543, thestatus code returned by the server to the client 545, the size of theobject returned to the client (in this case, the gif file requested bythe client) 546 and the time spent to serve the request in microseconds544. As seen in FIG. 5C, all the raw machine data retrieved from theserver access log is retained and stored as part of the correspondingevents, 1221, 1222, and 1223 in the data store.

Event 534 is associated with an entry in a server error log, asindicated by “error.log” in the source column 537 that records errorsthat the server encountered when processing a client request. Similar tothe events related to the server access log, all the raw machine data inthe error log file pertaining to event 534 can be preserved and storedas part of the event 534.

Saving minimally processed or unprocessed machine data in a data storeassociated with metadata fields in the manner similar to that shown inFIG. 5C is advantageous because it allows search of all the machine dataat search time instead of searching only previously specified andidentified fields or field-value pairs. As mentioned above, because datastructures used by various embodiments of the present disclosuremaintain the underlying raw machine data and use a late-binding schemafor searching the raw machines data, it enables a user to continueinvestigating and learn valuable insights about the raw data. In otherwords, the user is not compelled to know about all the fields ofinformation that will be needed at data ingestion time. As a user learnsmore about the data in the events, the user can continue to refine thelate-binding schema by defining new extraction rules, or modifying ordeleting existing extraction rules used by the system.

3.7.3. Indexing

At blocks 514 and 516, an indexer can optionally generate a keywordindex to facilitate fast keyword searching for events. To build akeyword index, at block 514, the indexer identifies a set of keywords ineach event. At block 516, the indexer includes the identified keywordsin an index, which associates each stored keyword with referencepointers to events containing that keyword (or to locations withinevents where that keyword is located, other location identifiers, etc.).When an indexer subsequently receives a keyword-based query, the indexercan access the keyword index to quickly identify events containing thekeyword.

In some embodiments, the keyword index may include entries for fieldname-value pairs found in events, where a field name-value pair caninclude a pair of keywords connected by a symbol, such as an equals signor colon. This way, events containing these field name-value pairs canbe quickly located. In some embodiments, fields can automatically begenerated for some or all of the field names of the field name-valuepairs at the time of indexing. For example, if the string“dest=10.0.1.2” is found in an event, a field named “dest” may becreated for the event, and assigned a value of “10.0.1.2”.

At block 518, the indexer stores the events with an associated timestampin a data store 208. Timestamps enable a user to search for events basedon a time range. In some embodiments, the stored events are organizedinto “buckets,” where each bucket stores events associated with aspecific time range based on the timestamps associated with each event.This improves time-based searching, as well as allows for events withrecent timestamps, which may have a higher likelihood of being accessed,to be stored in a faster memory to facilitate faster retrieval. Forexample, buckets containing the most recent events can be stored inflash memory rather than on a hard disk. In some embodiments, eachbucket may be associated with an identifier, a time range, and a sizeconstraint. In certain embodiments, a bucket can correspond to a filesystem directory and the machine data, or events, of a bucket can bestored in one or more files of the file system directory. The filesystem directory can include additional files, such as one or moreinverted indexes, high performance indexes, permissions files,configuration files, etc.

Each indexer 206 may be responsible for storing and searching a subsetof the events contained in a corresponding data store 208. Bydistributing events among the indexers and data stores, the indexers cananalyze events for a query in parallel. For example, using map-reducetechniques, each indexer returns partial responses for a subset ofevents to a search head that combines the results to produce an answerfor the query. By storing events in buckets for specific time ranges, anindexer may further optimize the data retrieval process by searchingbuckets corresponding to time ranges that are relevant to a query.

In some embodiments, each indexer has a home directory and a colddirectory. The home directory of an indexer stores hot buckets and warmbuckets, and the cold directory of an indexer stores cold buckets. A hotbucket is a bucket that is capable of receiving and storing events. Awarm bucket is a bucket that can no longer receive events for storagebut has not yet been moved to the cold directory. A cold bucket is abucket that can no longer receive events and may be a bucket that waspreviously stored in the home directory. The home directory may bestored in faster memory, such as flash memory, as events may be activelywritten to the home directory, and the home directory may typicallystore events that are more frequently searched and thus are accessedmore frequently. The cold directory may be stored in slower and/orlarger memory, such as a hard disk, as events are no longer beingwritten to the cold directory, and the cold directory may typicallystore events that are not as frequently searched and thus are accessedless frequently. In some embodiments, an indexer may also have aquarantine bucket that contains events having potentially inaccurateinformation, such as an incorrect time stamp associated with the eventor a time stamp that appears to be an unreasonable time stamp for thecorresponding event. The quarantine bucket may have events from any timerange; as such, the quarantine bucket may always be searched at searchtime. Additionally, an indexer may store old, archived data in a frozenbucket that is not capable of being searched at search time. In someembodiments, a frozen bucket may be stored in slower and/or largermemory, such as a hard disk, and may be stored in offline and/or remotestorage.

Moreover, events and buckets can also be replicated across differentindexers and data stores to facilitate high availability and disasterrecovery as described in U.S. Pat. No. 9,130,971, entitled “SITE-BASEDSEARCH AFFINITY”, issued on 8 Sep. 2015, and in U.S. patent Ser. No.14/266,817, entitled “MULTI-SITE CLUSTERING”, issued on 1 Sep. 2015,each of which is hereby incorporated by reference in its entirety forall purposes.

As will be described in greater detail below with reference to, interalia, FIGS. 18-49 , some functionality of the indexer can be handled bydifferent components of the system. For example, in some cases, theindexer indexes semi-processed, or cooked data (e.g., data that has beenparsed and/or had some fields determined for it), and stores the resultsin common storage.

FIG. 5B is a block diagram of an example data store 501 that includes adirectory for each index (or partition) that contains a portion of datamanaged by an indexer. FIG. 5B further illustrates details of anembodiment of an inverted index 507B and an event reference array 515associated with inverted index 507B.

The data store 501 can correspond to a data store 208 that stores eventsmanaged by an indexer 206 or can correspond to a different data storeassociated with an indexer 206. In the illustrated embodiment, the datastore 501 includes a _main directory 503 associated with a _main indexand a _test directory 505 associated with a _test index. However, thedata store 501 can include fewer or more directories. In someembodiments, multiple indexes can share a single directory or allindexes can share a common directory. Additionally, although illustratedas a single data store 501, it will be understood that the data store501 can be implemented as multiple data stores storing differentportions of the information shown in FIG. 5B. For example, a singleindex or partition can span multiple directories or multiple datastores, and can be indexed or searched by multiple correspondingindexers.

In the illustrated embodiment of FIG. 5B, the index-specific directories503 and 505 include inverted indexes 507A, 507B and 509A, 509B,respectively. The inverted indexes 507A . . . 507B, and 509A . . . 509Bcan be keyword indexes or field-value pair indexes described herein andcan include less or more information that depicted in FIG. 5B.

In some embodiments, each inverted index 507A . . . 507B, and 509A . . .509B can correspond to a distinct time-series bucket that is managed bythe indexer 206 and that contains events corresponding to the relevantindex (e.g., _main index, _test index). As such, each inverted index cancorrespond to a particular range of time for an index. Additional files,such as high performance indexes for each time-series bucket of anindex, can also be stored in the same directory as the inverted indexes507A . . . 507B, and 509A . . . 509B. In some embodiments inverted index507A . . . 507B, and 509A . . . 509B can correspond to multipletime-series buckets or inverted indexes 507A . . . 507B, and 509A . . .509B can correspond to a single time-series bucket.

Each inverted index 507A . . . 507B, and 509A . . . 509B can include oneor more entries, such as keyword (or token) entries or field-value pairentries. Furthermore, in certain embodiments, the inverted indexes 507A. . . 507B, and 509A . . . 509B can include additional information, suchas a time range 523 associated with the inverted index or an indexidentifier 525 identifying the index associated with the inverted index507A . . . 507B, and 509A . . . 509B. However, each inverted index 507A. . . 507B, and 509A . . . 509B can include less or more informationthan depicted.

Token entries, such as token entries 511 illustrated in inverted index507B, can include a token 511A (e.g., “error,” “itemID,” etc.) and eventreferences 511B indicative of events that include the token. Forexample, for the token “error,” the corresponding token entry includesthe token “error” and an event reference, or unique identifier, for eachevent stored in the corresponding time-series bucket that includes thetoken “error.” In the illustrated embodiment of FIG. 5B, the error tokenentry includes the identifiers 3, 5, 6, 8, 11, and 12 corresponding toevents managed by the indexer 206 and associated with the index _main503 that are located in the time-series bucket associated with theinverted index 507B.

In some cases, some token entries can be default entries, automaticallydetermined entries, or user specified entries. In some embodiments, theindexer 206 can identify each word or string in an event as a distincttoken and generate a token entry for it. In some cases, the indexer 206can identify the beginning and ending of tokens based on punctuation,spaces, as described in greater detail herein. In certain cases, theindexer 206 can rely on user input or a configuration file to identifytokens for token entries 511, etc. It will be understood that anycombination of token entries can be included as a default, automaticallydetermined, a or included based on user-specified criteria.

Similarly, field-value pair entries, such as field-value pair entries513 shown in inverted index 507B, can include a field-value pair 513Aand event references 513B indicative of events that include a fieldvalue that corresponds to the field-value pair. For example, for afield-value pair sourcetype::sendmail, a field-value pair entry wouldinclude the field-value pair sourcetype::sendmail and a uniqueidentifier, or event reference, for each event stored in thecorresponding time-series bucket that includes a sendmail sourcetype.

In some cases, the field-value pair entries 513 can be default entries,automatically determined entries, or user specified entries. As anon-limiting example, the field-value pair entries for the fields host,source, sourcetype can be included in the inverted indexes 507A . . .507B, and 509A . . . 509B as a default. As such, all of the invertedindexes 507A . . . 507B, and 509A . . . 509B can include field-valuepair entries for the fields host, source, sourcetype. As yet anothernon-limiting example, the field-value pair entries for the IP_addressfield can be user specified and may only appear in the inverted index507B based on user-specified criteria. As another non-limiting example,as the indexer indexes the events, it can automatically identifyfield-value pairs and create field-value pair entries. For example,based on the indexers review of events, it can identify IP_address as afield in each event and add the IP_address field-value pair entries tothe inverted index 507B. It will be understood that any combination offield-value pair entries can be included as a default, automaticallydetermined, or included based on user-specified criteria.

Each unique identifier 517, or event reference, can correspond to aunique event located in the time series bucket. However, the same eventreference can be located in multiple entries. For example if an eventhas a sourcetype splunkd, host www1 and token “warning,” then the uniqueidentifier for the event will appear in the field-value pair entriessourcetype::splunkd and host::www1, as well as the token entry“warning.” With reference to the illustrated embodiment of FIG. 5B andthe event that corresponds to the event reference 3, the event reference3 is found in the field-value pair entries 513 host::hostA,source::sourceB, sourcetype::sourcetypeA, and IP_address::91.205.189.15indicating that the event corresponding to the event reference 3 is fromhostA, sourceB, of sourcetypeA, and includes 91.205.189.15 in the eventdata.

For some fields, the unique identifier is located in only onefield-value pair entry for a particular field. For example, the invertedindex may include four sourcetype field-value pair entries correspondingto four different sourcetypes of the events stored in a bucket (e.g.,sourcetypes: sendmail, splunkd, web_access, and web_service). Withinthose four sourcetype field-value pair entries, an identifier for aparticular event may appear in only one of the field-value pair entries.With continued reference to the example illustrated embodiment of FIG.5B, since the event reference 7 appears in the field-value pair entrysourcetype::sourcetypeA, then it does not appear in the otherfield-value pair entries for the sourcetype field, includingsourcetype::sourcetypeB, sourcetype::sourcetypeC, andsourcetype::sourcetypeD.

The event references 517 can be used to locate the events in thecorresponding bucket. For example, the inverted index can include, or beassociated with, an event reference array 515. The event reference array515 can include an array entry 517 for each event reference in theinverted index 507B. Each array entry 517 can include locationinformation 519 of the event corresponding to the unique identifier(non-limiting example: seek address of the event), a timestamp 521associated with the event, or additional information regarding the eventassociated with the event reference, etc.

For each token entry 511 or field-value pair entry 513, the eventreference 501B or unique identifiers can be listed in chronologicalorder or the value of the event reference can be assigned based onchronological data, such as a timestamp associated with the eventreferenced by the event reference. For example, the event reference 1 inthe illustrated embodiment of FIG. 5B can correspond to thefirst-in-time event for the bucket, and the event reference 12 cancorrespond to the last-in-time event for the bucket. However, the eventreferences can be listed in any order, such as reverse chronologicalorder, ascending order, descending order, or some other order, etc.Further, the entries can be sorted. For example, the entries can besorted alphabetically (collectively or within a particular group), byentry origin (e.g., default, automatically generated, user-specified,etc.), by entry type (e.g., field-value pair entry, token entry, etc.),or chronologically by when added to the inverted index, etc. In theillustrated embodiment of FIG. 5B, the entries are sorted first by entrytype and then alphabetically.

As a non-limiting example of how the inverted indexes 507A . . . 507B,and 509A . . . 509B can be used during a data categorization requestcommand, the indexers can receive filter criteria indicating data thatis to be categorized and categorization criteria indicating how the datais to be categorized. Example filter criteria can include, but is notlimited to, indexes (or partitions), hosts, sources, sourcetypes, timeranges, field identifier, keywords, etc.

Using the filter criteria, the indexer identifies relevant invertedindexes to be searched. For example, if the filter criteria includes aset of partitions, the indexer can identify the inverted indexes storedin the directory corresponding to the particular partition as relevantinverted indexes. Other means can be used to identify inverted indexesassociated with a partition of interest. For example, in someembodiments, the indexer can review an entry in the inverted indexes,such as an index-value pair entry 513 to determine if a particularinverted index is relevant. If the filter criteria does not identify anypartition, then the indexer can identify all inverted indexes managed bythe indexer as relevant inverted indexes.

Similarly, if the filter criteria includes a time range, the indexer canidentify inverted indexes corresponding to buckets that satisfy at leasta portion of the time range as relevant inverted indexes. For example,if the time range is last hour then the indexer can identify allinverted indexes that correspond to buckets storing events associatedwith timestamps within the last hour as relevant inverted indexes.

When used in combination, an index filter criterion specifying one ormore partitions and a time range filter criterion specifying aparticular time range can be used to identify a subset of invertedindexes within a particular directory (or otherwise associated with aparticular partition) as relevant inverted indexes. As such, the indexercan focus the processing to only a subset of the total number ofinverted indexes that the indexer manages.

Once the relevant inverted indexes are identified, the indexer canreview them using any additional filter criteria to identify events thatsatisfy the filter criteria. In some cases, using the known location ofthe directory in which the relevant inverted indexes are located, theindexer can determine that any events identified using the relevantinverted indexes satisfy an index filter criterion. For example, if thefilter criteria includes a partition main, then the indexer candetermine that any events identified using inverted indexes within thepartition main directory (or otherwise associated with the partitionmain) satisfy the index filter criterion.

Furthermore, based on the time range associated with each invertedindex, the indexer can determine that that any events identified using aparticular inverted index satisfies a time range filter criterion. Forexample, if a time range filter criterion is for the last hour and aparticular inverted index corresponds to events within a time range of50 minutes ago to 35 minutes ago, the indexer can determine that anyevents identified using the particular inverted index satisfy the timerange filter criterion. Conversely, if the particular inverted indexcorresponds to events within a time range of 59 minutes ago to 62minutes ago, the indexer can determine that some events identified usingthe particular inverted index may not satisfy the time range filtercriterion.

Using the inverted indexes, the indexer can identify event references(and therefore events) that satisfy the filter criteria. For example, ifthe token “error” is a filter criterion, the indexer can track all eventreferences within the token entry “error.” Similarly, the indexer canidentify other event references located in other token entries orfield-value pair entries that match the filter criteria. The system canidentify event references located in all of the entries identified bythe filter criteria. For example, if the filter criteria include thetoken “error” and field-value pair sourcetype::web_ui, the indexer cantrack the event references found in both the token entry “error” and thefield-value pair entry sourcetype::web_ui. As mentioned previously, insome cases, such as when multiple values are identified for a particularfilter criterion (e.g., multiple sources for a source filter criterion),the system can identify event references located in at least one of theentries corresponding to the multiple values and in all other entriesidentified by the filter criteria. The indexer can determine that theevents associated with the identified event references satisfy thefilter criteria.

In some cases, the indexer can further consult a timestamp associatedwith the event reference to determine whether an event satisfies thefilter criteria. For example, if an inverted index corresponds to a timerange that is partially outside of a time range filter criterion, thenthe indexer can consult a timestamp associated with the event referenceto determine whether the corresponding event satisfies the time rangecriterion. In some embodiments, to identify events that satisfy a timerange, the indexer can review an array, such as the event referencearray 1614 that identifies the time associated with the events.Furthermore, as mentioned above using the known location of thedirectory in which the relevant inverted indexes are located (or otherindex identifier), the indexer can determine that any events identifiedusing the relevant inverted indexes satisfy the index filter criterion.

In some cases, based on the filter criteria, the indexer reviews anextraction rule. In certain embodiments, if the filter criteria includesa field name that does not correspond to a field-value pair entry in aninverted index, the indexer can review an extraction rule, which may belocated in a configuration file, to identify a field that corresponds toa field-value pair entry in the inverted index.

For example, the filter criteria includes a field name “sessionID” andthe indexer determines that at least one relevant inverted index doesnot include a field-value pair entry corresponding to the field namesessionID, the indexer can review an extraction rule that identifies howthe sessionID field is to be extracted from a particular host, source,or sourcetype (implicitly identifying the particular host, source, orsourcetype that includes a sessionID field). The indexer can replace thefield name “sessionID” in the filter criteria with the identified host,source, or sourcetype. In some cases, the field name “sessionID” may beassociated with multiples hosts, sources, or sourcetypes, in which case,all identified hosts, sources, and sourcetypes can be added as filtercriteria. In some cases, the identified host, source, or sourcetype canreplace or be appended to a filter criterion, or be excluded. Forexample, if the filter criteria includes a criterion for source S1 andthe “sessionID” field is found in source S2, the source S2 can replaceS1 in the filter criteria, be appended such that the filter criteriaincludes source S1 and source S2, or be excluded based on the presenceof the filter criterion source S1. If the identified host, source, orsourcetype is included in the filter criteria, the indexer can thenidentify a field-value pair entry in the inverted index that includes afield value corresponding to the identity of the particular host,source, or sourcetype identified using the extraction rule.

Once the events that satisfy the filter criteria are identified, thesystem, such as the indexer 206 can categorize the results based on thecategorization criteria. The categorization criteria can includecategories for grouping the results, such as any combination ofpartition, source, sourcetype, or host, or other categories or fields asdesired.

The indexer can use the categorization criteria to identifycategorization criteria-value pairs or categorization criteria values bywhich to categorize or group the results. The categorizationcriteria-value pairs can correspond to one or more field-value pairentries stored in a relevant inverted index, one or more index-valuepairs based on a directory in which the inverted index is located or anentry in the inverted index (or other means by which an inverted indexcan be associated with a partition), or other criteria-value pair thatidentifies a general category and a particular value for that category.The categorization criteria values can correspond to the value portionof the categorization criteria-value pair.

As mentioned, in some cases, the categorization criteria-value pairs cancorrespond to one or more field-value pair entries stored in therelevant inverted indexes. For example, the categorizationcriteria-value pairs can correspond to field-value pair entries of host,source, and sourcetype (or other field-value pair entry as desired). Forinstance, if there are ten different hosts, four different sources, andfive different sourcetypes for an inverted index, then the invertedindex can include ten host field-value pair entries, four sourcefield-value pair entries, and five sourcetype field-value pair entries.The indexer can use the nineteen distinct field-value pair entries ascategorization criteria-value pairs to group the results.

Specifically, the indexer can identify the location of the eventreferences associated with the events that satisfy the filter criteriawithin the field-value pairs, and group the event references based ontheir location. As such, the indexer can identify the particular fieldvalue associated with the event corresponding to the event reference.For example, if the categorization criteria include host and sourcetype,the host field-value pair entries and sourcetype field-value pairentries can be used as categorization criteria-value pairs to identifythe specific host and sourcetype associated with the events that satisfythe filter criteria.

In addition, as mentioned, categorization criteria-value pairs cancorrespond to data other than the field-value pair entries in therelevant inverted indexes. For example, if partition or index is used asa categorization criterion, the inverted indexes may not includepartition field-value pair entries. Rather, the indexer can identify thecategorization criteria-value pair associated with the partition basedon the directory in which an inverted index is located, information inthe inverted index, or other information that associates the invertedindex with the partition, etc. As such a variety of methods can be usedto identify the categorization criteria-value pairs from thecategorization criteria.

Accordingly based on the categorization criteria (and categorizationcriteria-value pairs), the indexer can generate groupings based on theevents that satisfy the filter criteria. As a non-limiting example, ifthe categorization criteria includes a partition and sourcetype, thenthe groupings can correspond to events that are associated with eachunique combination of partition and sourcetype. For instance, if thereare three different partitions and two different sourcetypes associatedwith the identified events, then the six different groups can be formed,each with a unique partition value-sourcetype value combination.Similarly, if the categorization criteria includes partition,sourcetype, and host and there are two different partitions, threesourcetypes, and five hosts associated with the identified events, thenthe indexer can generate up to thirty groups for the results thatsatisfy the filter criteria. Each group can be associated with a uniquecombination of categorization criteria-value pairs (e.g., uniquecombinations of partition value sourcetype value, and host value).

In addition, the indexer can count the number of events associated witheach group based on the number of events that meet the uniquecombination of categorization criteria for a particular group (or matchthe categorization criteria-value pairs for the particular group). Withcontinued reference to the example above, the indexer can count thenumber of events that meet the unique combination of partition,sourcetype, and host for a particular group.

Each indexer communicates the groupings to the search head. The searchhead can aggregate the groupings from the indexers and provide thegroupings for display. In some cases, the groups are displayed based onat least one of the host, source, sourcetype, or partition associatedwith the groupings. In some embodiments, the search head can furtherdisplay the groups based on display criteria, such as a display order ora sort order as described in greater detail above.

As a non-limiting example and with reference to FIG. 5B, consider arequest received by an indexer 206 that includes the following filtercriteria: keyword=error, partition=_main, time range=3/1/1716:22.00.000-16:28.00.000, sourcetype=sourcetypeC, host=hostB, and thefollowing categorization criteria: source.

Based on the above criteria, the indexer 206 identifies _main directory503 and can ignore _test directory 505 and any other partition-specificdirectories. The indexer determines that inverted partition 507B is arelevant partition based on its location within the _main directory 503and the time range associated with it. For sake of simplicity in thisexample, the indexer 206 determines that no other inverted indexes inthe _main directory 503, such as inverted index 507A satisfy the timerange criterion.

Having identified the relevant inverted index 507B, the indexer reviewsthe token entries 511 and the field-value pair entries 513 to identifyevent references, or events, that satisfy all of the filter criteria.

With respect to the token entries 511, the indexer can review the errortoken entry and identify event references 3, 5, 6, 8, 11, 12, indicatingthat the term “error” is found in the corresponding events. Similarly,the indexer can identify event references 4, 5, 6, 8, 9, 10, 11 in thefield-value pair entry sourcetype::sourcetypeC and event references 2,5, 6, 8, 10, 11 in the field-value pair entry host::hostB. As the filtercriteria did not include a source or an IP_address field-value pair, theindexer can ignore those field-value pair entries.

In addition to identifying event references found in at least one tokenentry or field-value pair entry (e.g., event references 3, 4, 5, 6, 8,9, 10, 11, 12), the indexer can identify events (and corresponding eventreferences) that satisfy the time range criterion using the eventreference array 1614 (e.g., event references 2, 3, 4, 5, 6, 7, 8, 9,10). Using the information obtained from the inverted index 507B(including the event reference array 515), the indexer 206 can identifythe event references that satisfy all of the filter criteria (e.g.,event references 5, 6, 8).

Having identified the events (and event references) that satisfy all ofthe filter criteria, the indexer 206 can group the event referencesusing the received categorization criteria (source). In doing so, theindexer can determine that event references 5 and 6 are located in thefield-value pair entry source::sourceD (or have matching categorizationcriteria-value pairs) and event reference 8 is located in thefield-value pair entry source::sourceC. Accordingly, the indexer cangenerate a sourceC group having a count of one corresponding toreference 8 and a sourceD group having a count of two corresponding toreferences 5 and 6. This information can be communicated to the searchhead. In turn the search head can aggregate the results from the variousindexers and display the groupings. As mentioned above, in someembodiments, the groupings can be displayed based at least in part onthe categorization criteria, including at least one of host, source,sourcetype, or partition.

It will be understood that a change to any of the filter criteria orcategorization criteria can result in different groupings. As a onenon-limiting example, a request received by an indexer 206 that includesthe following filter criteria: partition=_main, time range=3/1/17 3/1/1716:21:20.000-16:28:17.000, and the following categorization criteria:host, source, sourcetype would result in the indexer identifying eventreferences 1-12 as satisfying the filter criteria. The indexer wouldthen generate up to 24 groupings corresponding to the 24 differentcombinations of the categorization criteria-value pairs, including host(hostA, hostB), source (sourceA, sourceB, sourceC, sourceD), andsourcetype (sourcetypeA, sourcetypeB, sourcetypeC). However, as thereare only twelve events identifiers in the illustrated embodiment andsome fall into the same grouping, the indexer generates eight groups andcounts as follows:

Group 1 (hostA, sourceA, sourcetypeA): 1 (event reference 7)Group 2 (hostA, sourceA, sourcetypeB): 2 (event references 1, 12)Group 3 (hostA, sourceA, sourcetypeC): 1 (event reference 4)Group 4 (hostA, sourceB, sourcetypeA): 1 (event reference 3)Group 5 (hostA, sourceB, sourcetypeC): 1 (event reference 9)Group 6 (hostB, sourceC, sourcetypeA): 1 (event reference 2)Group 7 (hostB, sourceC, sourcetypeC): 2 (event references 8, 11)Group 8 (hostB, sourceD, sourcetypeC): 3 (event references 5, 6, 10)

As noted, each group has a unique combination of categorizationcriteria-value pairs or categorization criteria values. The indexercommunicates the groups to the search head for aggregation with resultsreceived from other indexers. In communicating the groups to the searchhead, the indexer can include the categorization criteria-value pairsfor each group and the count. In some embodiments, the indexer caninclude more or less information. For example, the indexer can includethe event references associated with each group and other identifyinginformation, such as the indexer or inverted index used to identify thegroups.

As another non-limiting examples, a request received by an indexer 206that includes the following filter criteria: partition=_main, timerange=3/1/17 3/1/17 16:21:20.000-16:28:17.000, source=sourceA, sourceD,and keyword=itemID and the following categorization criteria: host,source, sourcetype would result in the indexer identifying eventreferences 4, 7, and 10 as satisfying the filter criteria, and generatethe following groups:

Group 1 (hostA, sourceA, sourcetypeC): 1 (event reference 4)Group 2 (hostA, sourceA, sourcetypeA): 1 (event reference 7)Group 3 (hostB, sourceD, sourcetypeC): 1 (event references 10)

The indexer communicates the groups to the search head for aggregationwith results received from other indexers. As will be understand thereare myriad ways for filtering and categorizing the events and eventreferences. For example, the indexer can review multiple invertedindexes associated with an partition or review the inverted indexes ofmultiple partitions, and categorize the data using any one or anycombination of partition, host, source, sourcetype, or other category,as desired.

Further, if a user interacts with a particular group, the indexer canprovide additional information regarding the group. For example, theindexer can perform a targeted search or sampling of the events thatsatisfy the filter criteria and the categorization criteria for theselected group, also referred to as the filter criteria corresponding tothe group or filter criteria associated with the group.

In some cases, to provide the additional information, the indexer relieson the inverted index. For example, the indexer can identify the eventreferences associated with the events that satisfy the filter criteriaand the categorization criteria for the selected group and then use theevent reference array 515 to access some or all of the identifiedevents. In some cases, the categorization criteria values orcategorization criteria-value pairs associated with the group becomepart of the filter criteria for the review.

With reference to FIG. 5B for instance, suppose a group is displayedwith a count of six corresponding to event references 4, 5, 6, 8, 10, 11(e.g., event references 4, 5, 6, 8, 10, 11 satisfy the filter criteriaand are associated with matching categorization criteria values orcategorization criteria-value pairs) and a user interacts with the group(e.g., selecting the group, clicking on the group, etc.). In response,the search head communicates with the indexer to provide additionalinformation regarding the group.

In some embodiments, the indexer identifies the event referencesassociated with the group using the filter criteria and thecategorization criteria for the group (e.g., categorization criteriavalues or categorization criteria-value pairs unique to the group).Together, the filter criteria and the categorization criteria for thegroup can be referred to as the filter criteria associated with thegroup. Using the filter criteria associated with the group, the indexeridentifies event references 4, 5, 6, 8, 10, 11.

Based on a sampling criteria, discussed in greater detail above, theindexer can determine that it will analyze a sample of the eventsassociated with the event references 4, 5, 6, 8, 10, 11. For example,the sample can include analyzing event data associated with the eventreferences 5, 8, 10. In some embodiments, the indexer can use the eventreference array 1616 to access the event data associated with the eventreferences 5, 8, 10. Once accessed, the indexer can compile the relevantinformation and provide it to the search head for aggregation withresults from other indexers. By identifying events and sampling eventdata using the inverted indexes, the indexer can reduce the amount ofactual data this is analyzed and the number of events that are accessedin order to generate the summary of the group and provide a response inless time.

3.8. Query Processing

FIG. 6A is a flow diagram of an example method that illustrates how asearch head and indexers perform a search query, in accordance withexample embodiments. At block 602, a search head receives a search queryfrom a client. At block 604, the search head analyzes the search queryto determine what portion(s) of the query can be delegated to indexersand what portions of the query can be executed locally by the searchhead. At block 606, the search head distributes the determined portionsof the query to the appropriate indexers. In some embodiments, a searchhead cluster may take the place of an independent search head where eachsearch head in the search head cluster coordinates with peer searchheads in the search head cluster to schedule jobs, replicate searchresults, update configurations, fulfill search requests, etc. In someembodiments, the search head (or each search head) communicates with amaster node (also known as a cluster master, not shown in FIG. 2 ) thatprovides the search head with a list of indexers to which the searchhead can distribute the determined portions of the query. The masternode maintains a list of active indexers and can also designate whichindexers may have responsibility for responding to queries over certainsets of events. A search head may communicate with the master nodebefore the search head distributes queries to indexers to discover theaddresses of active indexers.

At block 608, the indexers to which the query was distributed, searchdata stores associated with them for events that are responsive to thequery. To determine which events are responsive to the query, theindexer searches for events that match the criteria specified in thequery. These criteria can include matching keywords or specific valuesfor certain fields. The searching operations at block 608 may use thelate-binding schema to extract values for specified fields from eventsat the time the query is processed. In some embodiments, one or morerules for extracting field values may be specified as part of a sourcetype definition in a configuration file. The indexers may then eithersend the relevant events back to the search head, or use the events todetermine a partial result, and send the partial result back to thesearch head.

At block 610, the search head combines the partial results and/or eventsreceived from the indexers to produce a final result for the query. Insome examples, the results of the query are indicative of performance orsecurity of the IT environment and may help improve the performance ofcomponents in the IT environment. This final result may comprisedifferent types of data depending on what the query requested. Forexample, the results can include a listing of matching events returnedby the query, or some type of visualization of the data from thereturned events. In another example, the final result can include one ormore calculated values derived from the matching events.

The results generated by the system 108 can be returned to a clientusing different techniques. For example, one technique streams resultsor relevant events back to a client in real-time as they are identified.Another technique waits to report the results to the client until acomplete set of results (which may include a set of relevant events or aresult based on relevant events) is ready to return to the client. Yetanother technique streams interim results or relevant events back to theclient in real-time until a complete set of results is ready, and thenreturns the complete set of results to the client. In another technique,certain results are stored as “search jobs” and the client may retrievethe results by referring the search jobs.

The search head can also perform various operations to make the searchmore efficient. For example, before the search head begins execution ofa query, the search head can determine a time range for the query and aset of common keywords that all matching events include. The search headmay then use these parameters to query the indexers to obtain a supersetof the eventual results. Then, during a filtering stage, the search headcan perform field-extraction operations on the superset to produce areduced set of search results. This speeds up queries, which may beparticularly helpful for queries that are performed on a periodic basis.

As will be described in greater detail below with reference to, interalia, FIGS. 18-49 , some functionality of the search head or indexerscan be handled by different components of the system or removedaltogether. For example, in some cases, a query coordinator analyzes thequery, identifies dataset sources to be accessed, generates subqueriesfor execution by dataset sources, such as indexers, collects partialresults to produce a final result and returns the final results to thesearch head for delivery to a client device or delivers the finalresults to the client device without the search head. In some cases,results from dataset sources, such as the indexers, are communicated tonodes, which further process the data, and communicate the results ofthe processing to the query coordinator, etc. In some embodiments, thesearch head spawns a search process, which communicates the query to asearch process master. The search process master can communicate thequery to the query coordinator for processing and execution.

In addition, in some embodiments, the indexers are not involved insearch operations or only search some data, such as data in hot buckets,etc. For example, nodes can perform the search functionality describedherein with respect to indexers. For example, nodes can use late-bindingschema to extract values for specified fields from events at the timethe query is processed and/or use one or more rules specified as part ofa source type definition in a configuration file for extracting fieldvalues, etc. Furthermore, in some embodiments, nodes can perform searchoperations on data in common storage or found in other dataset sources,such as external data stores, query acceleration data stores, ingesteddata buffers, etc.

3.9. Pipelined Search Language

Various embodiments of the present disclosure can be implemented using,or in conjunction with, a pipelined command language. A pipelinedcommand language is a language in which a set of inputs or data isoperated on by a first command in a sequence of commands, and thensubsequent commands in the order they are arranged in the sequence. Suchcommands can include any type of functionality for operating on data,such as retrieving, searching, filtering, aggregating, processing,transmitting, and the like. As described herein, a query can thus beformulated in a pipelined command language and include any number ofordered or unordered commands for operating on data.

Splunk Processing Language (SPL) is an example of a pipelined commandlanguage in which a set of inputs or data is operated on by any numberof commands in a particular sequence. A sequence of commands, or commandsequence, can be formulated such that the order in which the commandsare arranged defines the order in which the commands are applied to aset of data or the results of an earlier executed command. For example,a first command in a command sequence can operate to search or filterfor specific data in particular set of data. The results of the firstcommand can then be passed to another command listed later in thecommand sequence for further processing.

In various embodiments, a query can be formulated as a command sequencedefined in a command line of a search UI. In some embodiments, a querycan be formulated as a sequence of SPL commands. Some or all of the SPLcommands in the sequence of SPL commands can be separated from oneanother by a pipe symbol “|”. In such embodiments, a set of data, suchas a set of events, can be operated on by a first SPL command in thesequence, and then a subsequent SPL command following a pipe symbol “|”after the first SPL command operates on the results produced by thefirst SPL command or other set of data, and so on for any additional SPLcommands in the sequence. As such, a query formulated using SPLcomprises a series of consecutive commands that are delimited by pipe“|” characters. The pipe character indicates to the system that theoutput or result of one command (to the left of the pipe) should be usedas the input for one of the subsequent commands (to the right of thepipe). This enables formulation of queries defined by a pipeline ofsequenced commands that refines or enhances the data at each step alongthe pipeline until the desired results are attained. Accordingly,various embodiments described herein can be implemented with SplunkProcessing Language (SPL) used in conjunction with the SPLUNK®ENTERPRISE system.

While a query can be formulated in many ways, a query can start with asearch command and one or more corresponding search terms at thebeginning of the pipeline. Such search terms can include any combinationof keywords, phrases, times, dates, Boolean expressions, fieldname-fieldvalue pairs, etc. that specify which results should be obtained from anindex. The results can then be passed as inputs into subsequent commandsin a sequence of commands by using, for example, a pipe character. Thesubsequent commands in a sequence can include directives for additionalprocessing of the results once it has been obtained from one or moreindexes. For example, commands may be used to filter unwantedinformation out of the results, extract more information, evaluate fieldvalues, calculate statistics, reorder the results, create an alert,create summary of the results, or perform some type of aggregationfunction. In some embodiments, the summary can include a graph, chart,metric, or other visualization of the data. An aggregation function caninclude analysis or calculations to return an aggregate value, such asan average value, a sum, a maximum value, a root mean square,statistical values, and the like.

Due to its flexible nature, use of a pipelined command language invarious embodiments is advantageous because it can perform “filtering”as well as “processing” functions. In other words, a single query caninclude a search command and search term expressions, as well asdata-analysis expressions. For example, a command at the beginning of aquery can perform a “filtering” step by retrieving a set of data basedon a condition (e.g., records associated with server response times ofless than 1 microsecond). The results of the filtering step can then bepassed to a subsequent command in the pipeline that performs a“processing” step (e.g. calculation of an aggregate value related to thefiltered events such as the average response time of servers withresponse times of less than 1 microsecond). Furthermore, the searchcommand can allow events to be filtered by keyword as well as fieldvalue criteria. For example, a search command can filter out all eventscontaining the word “warning” or filter out all events where a fieldvalue associated with a field “clientip” is “10.0.1.2.”

The results obtained or generated in response to a command in a querycan be considered a set of results data. The set of results data can bepassed from one command to another in any data format. In oneembodiment, the set of result data can be in the form of a dynamicallycreated table. Each command in a particular query can redefine the shapeof the table. In some implementations, an event retrieved from an indexin response to a query can be considered a row with a column for eachfield value. Columns contain basic information about the data and alsomay contain data that has been dynamically extracted at search time.

FIG. 6B provides a visual representation of the manner in which apipelined command language or query operates in accordance with thedisclosed embodiments. The command or query 630 can be inputted by theuser into a search field. The query comprises a search, the results ofwhich are piped to two commands (namely, command 1 and command 2) thatfollow the search step.

Disk 622 represents the event data in the raw record data store.

When a user query is processed, a search step will precede other queriesin the pipeline in order to generate a set of events at block 640. Forexample, the query can comprise search terms “sourcetype=syslog ERROR”at the front of the pipeline as shown in FIG. 6B. Intermediate resultstable 624 shows fewer rows because it represents the subset of eventsretrieved from the index that matched the search terms“sourcetype=syslog ERROR” from search command 630. By way of furtherexample, instead of a search step, the set of events at the head of thepipeline may be generating by a call to a pre-existing inverted index(as will be explained later).

At block 642, the set of events generated in the first part of the querymay be piped to a query that searches the set of events for field-valuepairs or for keywords. For example, the second intermediate resultstable 626 shows fewer columns, representing the result of the topcommand, “top user” which summarizes the events into a list of the top10 users and displays the user, count, and percentage.

Finally, at block 644, the results of the prior stage can be pipelinedto another stage where further filtering or processing of the data canbe performed, e.g., preparing the data for display purposes, filteringthe data based on a condition, performing a mathematical calculationwith the data, etc. As shown in FIG. 6B, the “fields—percent” part ofcommand 630 removes the column that shows the percentage, thereby,leaving a final results table 628 without a percentage column. Indifferent embodiments, other query languages, such as the StructuredQuery Language (“SQL”), can be used to create a query. In someembodiments, each stage can correspond to a search phase or layer in aDAG. The processing performed in each stage can be handled by one ormore partitions allocated to each stage.

3.10. Field Extraction

The search head 210 allows users to search and visualize eventsgenerated from machine data received from homogenous data sources. Thesearch head 210 also allows users to search and visualize eventsgenerated from machine data received from heterogeneous data sources.The search head 210 includes various mechanisms, which may additionallyreside in an indexer 206, for processing a query. A query language maybe used to create a query, such as any suitable pipelined querylanguage. For example, Splunk Processing Language (SPL) can be utilizedto make a query. SPL is a pipelined search language in which a set ofinputs is operated on by a first command in a command line, and then asubsequent command following the pipe symbol “|” operates on the resultsproduced by the first command, and so on for additional commands. Otherquery languages, such as the Structured Query Language (“SQL”), can beused to create a query.

In response to receiving the search query, search head 210 usesextraction rules to extract values for fields in the events beingsearched. The search head 210 obtains extraction rules that specify howto extract a value for fields from an event. Extraction rules cancomprise regex rules that specify how to extract values for the fieldscorresponding to the extraction rules. In addition to specifying how toextract field values, the extraction rules may also include instructionsfor deriving a field value by performing a function on a characterstring or value retrieved by the extraction rule. For example, anextraction rule may truncate a character string or convert the characterstring into a different data format. In some cases, the query itself canspecify one or more extraction rules.

The search head 210 can apply the extraction rules to events that itreceives from indexers 206. Indexers 206 may apply the extraction rulesto events in an associated data store 208. Extraction rules can beapplied to all the events in a data store or to a subset of the eventsthat have been filtered based on some criteria (e.g., event time stampvalues, etc.). Extraction rules can be used to extract one or morevalues for a field from events by parsing the portions of machine datain the events and examining the data for one or more patterns ofcharacters, numbers, delimiters, etc., that indicate where the fieldbegins and, optionally, ends.

As mentioned above, and as will be described in greater detail belowwith reference to, inter alia, FIGS. 18-49 , some functionality of thesearch head or indexers can be handled by different components of thesystem or removed altogether. For example, in some cases, a querycoordinator or nodes use extraction rules to extract values for fieldsin the events being searched. The query coordinator or nodes obtainextraction rules that specify how to extract a value for fields from anevent, etc., and apply the extraction rules to events that it receivesfrom indexers, common storage, ingested data buffers, query accelerationdata stores, or other dataset sources.

FIG. 7A is a diagram of an example scenario where a common customeridentifier is found among log data received from three disparate datasources, in accordance with example embodiments. In this example, a usersubmits an order for merchandise using a vendor's shopping applicationprogram 701 running on the user's system. In this example, the order wasnot delivered to the vendor's server due to a resource exception at thedestination server that is detected by the middleware code 702. The userthen sends a message to the customer support server 703 to complainabout the order failing to complete. The three systems 701, 702, and 703are disparate systems that do not have a common logging format. Theorder application 701 sends log data 704 to the data intake and querysystem in one format, the middleware code 702 sends error log data 705in a second format, and the support server 703 sends log data 706 in athird format.

Using the log data received at one or more indexers 206 from the threesystems, the vendor can uniquely obtain an insight into user activity,user experience, and system behavior. The search head 210 allows thevendor's administrator to search the log data from the three systemsthat one or more indexers 206 are responsible for searching, therebyobtaining correlated information, such as the order number andcorresponding customer ID number of the person placing the order. Thesystem also allows the administrator to see a visualization of relatedevents via a user interface. The administrator can query the search head210 for customer ID field value matches across the log data from thethree systems that are stored at the one or more indexers 206. Thecustomer ID field value exists in the data gathered from the threesystems, but the customer ID field value may be located in differentareas of the data given differences in the architecture of the systems.There is a semantic relationship between the customer ID field valuesgenerated by the three systems. The search head 210 requests events fromthe one or more indexers 206 to gather relevant events from the threesystems. The search head 210 then applies extraction rules to the eventsin order to extract field values that it can correlate. The search headmay apply a different extraction rule to each set of events from eachsystem when the event format differs among systems. In this example, theuser interface can display to the administrator the events correspondingto the common customer ID field values 707, 708, and 709, therebyproviding the administrator with insight into a customer's experience.

Note that query results can be returned to a client, a search head, orany other system component for further processing. In general, queryresults may include a set of one or more events, a set of one or morevalues obtained from the events, a subset of the values, statisticscalculated based on the values, a report containing the values, avisualization (e.g., a graph or chart) generated from the values, andthe like.

The search system enables users to run queries against the stored datato retrieve events that meet criteria specified in a query, such ascontaining certain keywords or having specific values in defined fields.FIG. 7B illustrates the manner in which keyword searches and fieldsearches are processed in accordance with disclosed embodiments.

If a user inputs a search query into search bar 710 that includes onlykeywords (also known as “tokens”), e.g., the keyword “error” or“warning”, the query search engine of the data intake and query systemsearches for those keywords directly in the event data 711 of the events713, 714, 715, 719 stored in the raw record data store. Note that whileFIG. 7B only illustrates four events, the raw record data store (whichmay to data store 208 in FIG. 2 ) may contain records for millions ofevents.

As disclosed above, an indexer can optionally generate a keyword indexto facilitate fast keyword searching for event data. The indexerincludes the identified keywords in an index, which associates eachstored keyword with reference pointers to events containing that keyword(or to locations within events where that keyword is located, otherlocation identifiers, etc.). When an indexer subsequently receives akeyword-based query, the indexer can access the keyword index to quicklyidentify events containing the keyword. For example, if the keyword“HTTP” was indexed by the indexer at index time, and the user searchesfor the keyword “HTTP”, events 713 to 715 will be identified based onthe results returned from the keyword index. As noted above, the indexcontains reference pointers to the events containing the keyword, whichallows for efficient retrieval of the relevant events from the rawrecord data store.

If a user searches for a keyword that has not been indexed by theindexer, the data intake and query system would nevertheless be able toretrieve the events by searching the event data for the keyword in theraw record data store directly as shown in FIG. 7B. For example, if auser searches for the keyword “frank”, and the name “frank” has not beenindexed at index time, the DATA INTAKE AND QUERY system will search theevent data directly and return the first event 713. Note that whetherthe keyword has been indexed at index time or not, in both cases the rawdata with the events 713, 714, 715, 719 is accessed from the raw datarecord store to service the keyword search. In the case where thekeyword has been indexed, the index will contain a reference pointerthat will allow for a more efficient retrieval of the event data fromthe data store. If the keyword has not been indexed, the search enginewill need to search through all the records in the data store to servicethe search.

In most cases, however, in addition to keywords, a user's search willalso include fields. The term “field” refers to a location in the eventdata containing one or more values for a specific data item. Often, afield is a value with a fixed, delimited position on a line, or a nameand value pair, where there is a single value to each field name. Afield can also be multivalued, that is, it can appear more than once inan event and have a different value for each appearance, e.g., emailaddress fields. Fields are searchable by the field name or fieldname-value pairs. Some examples of fields are “clientip” for IPaddresses accessing a web server, or the “From” and “To” fields in emailaddresses.

By way of further example, consider the search, “status=404”. Thissearch query finds events with “status” fields that have a value of“404.” When the search is run, the search engine does not look forevents with any other “status” value. It also does not look for eventscontaining other fields that share “404” as a value. As a result, thesearch returns a set of results that are more focused than if “404” hadbeen used in the search string as part of a keyword search. Note alsothat fields can appear in events as “key=value” pairs such as“user_name=Bob.” But in most cases, field values appear in fixed,delimited positions without identifying keys. For example, the datastore may contain events where the “user_name” value always appears byitself after the timestamp as illustrated by the following string: “Nov15 09:33:22 johnmedlock.”

The data intake and query system advantageously allows for search timefield extraction. In other words, fields can be extracted from the eventdata at search time using late-binding schema as opposed to at dataingestion time, which was a major limitation of the prior art systems.

In response to receiving the search query, search head 210 usesextraction rules to extract values for the fields associated with afield or fields in the event data being searched. The search head 210obtains extraction rules that specify how to extract a value for certainfields from an event. Extraction rules can comprise regex rules thatspecify how to extract values for the relevant fields. In addition tospecifying how to extract field values, the extraction rules may alsoinclude instructions for deriving a field value by performing a functionon a character string or value retrieved by the extraction rule. Forexample, a transformation rule may truncate a character string, orconvert the character string into a different data format. In somecases, the query itself can specify one or more extraction rules.

FIG. 7B illustrates the manner in which configuration files may be usedto configure custom fields at search time in accordance with thedisclosed embodiments. In response to receiving a search query, the dataintake and query system determines if the query references a “field.”For example, a query may request a list of events where the “clientip”field equals “127.0.0.1.” If the query itself does not specify anextraction rule and if the field is not a metadata field, e.g., time,host, source, source type, etc., then in order to determine anextraction rule, the search engine may, in one or more embodiments, needto locate configuration file 712 during the execution of the search asshown in FIG. 7B.

Configuration file 712 may contain extraction rules for all the variousfields that are not metadata fields, e.g., the “clientip” field. Theextraction rules may be inserted into the configuration file in avariety of ways. In some embodiments, the extraction rules can compriseregular expression rules that are manually entered in by the user.Regular expressions match patterns of characters in text and are usedfor extracting custom fields in text.

In one or more embodiments, as noted above, a field extractor may beconfigured to automatically generate extraction rules for certain fieldvalues in the events when the events are being created, indexed, orstored, or possibly at a later time. In one embodiment, a user may beable to dynamically create custom fields by highlighting portions of asample event that should be extracted as fields using a graphical userinterface. The system would then generate a regular expression thatextracts those fields from similar events and store the regularexpression as an extraction rule for the associated field in theconfiguration file 712.

In some embodiments, the indexers may automatically discover certaincustom fields at index time and the regular expressions for those fieldswill be automatically generated at index time and stored as part ofextraction rules in configuration file 712. For example, fields thatappear in the event data as “key=value” pairs may be automaticallyextracted as part of an automatic field discovery process. Note thatthere may be several other ways of adding field definitions toconfiguration files in addition to the methods discussed herein.

The search head 210 can apply the extraction rules derived fromconfiguration file 1402 to event data that it receives from indexers206. Indexers 206 may apply the extraction rules from the configurationfile to events in an associated data store 208. Extraction rules can beapplied to all the events in a data store, or to a subset of the eventsthat have been filtered based on some criteria (e.g., event time stampvalues, etc.). Extraction rules can be used to extract one or morevalues for a field from events by parsing the event data and examiningthe event data for one or more patterns of characters, numbers,delimiters, etc., that indicate where the field begins and, optionally,ends.

In one more embodiments, the extraction rule in configuration file 712will also need to define the type or set of events that the rule appliesto. Because the raw record data store will contain events from multipleheterogeneous sources, multiple events may contain the same fields indifferent locations because of discrepancies in the format of the datagenerated by the various sources. Furthermore, certain events may notcontain a particular field at all. For example, event 719 also contains“clientip” field, however, the “clientip” field is in a different formatfrom events 713-715. To address the discrepancies in the format andcontent of the different types of events, the configuration file willalso need to specify the set of events that an extraction rule appliesto, e.g., extraction rule 716 specifies a rule for filtering by the typeof event and contains a regular expression for parsing out the fieldvalue. Accordingly, each extraction rule will pertain to only aparticular type of event. If a particular field, e.g., “clientip” occursin multiple events, each of those types of events would need its owncorresponding extraction rule in the configuration file 712 and each ofthe extraction rules would comprise a different regular expression toparse out the associated field value. The most common way to categorizeevents is by source type because events generated by a particular sourcecan have the same format.

The field extraction rules stored in configuration file 712 performsearch-time field extractions. For example, for a query that requests alist of events with source type “access_combined” where the “clientip”field equals “127.0.0.1,” the query search engine would first locate theconfiguration file 712 to retrieve extraction rule 716 that would allowit to extract values associated with the “clientip” field from the eventdata 720 “where the source type is “access_combined. After the“clientip” field has been extracted from all the events comprising the“clientip” field where the source type is “access_combined,” the querysearch engine can then execute the field criteria by performing thecompare operation to filter out the events where the “clientip” fieldequals “127.0.0.1.” In the example shown in FIG. 7B, events 713-715would be returned in response to the user query. In this manner, thesearch engine can service queries containing field criteria in additionto queries containing keyword criteria (as explained above).

The configuration file can be created during indexing. It may either bemanually created by the user or automatically generated with certainpredetermined field extraction rules. As discussed above, the events maybe distributed across several indexers, wherein each indexer may beresponsible for storing and searching a subset of the events containedin a corresponding data store. In a distributed indexer system, eachindexer would need to maintain a local copy of the configuration filethat is synchronized periodically across the various indexers.

The ability to add schema to the configuration file at search timeresults in increased efficiency. A user can create new fields at searchtime and simply add field definitions to the configuration file. As auser learns more about the data in the events, the user can continue torefine the late-binding schema by adding new fields, deleting fields, ormodifying the field extraction rules in the configuration file for usethe next time the schema is used by the system. Because the data intakeand query system maintains the underlying raw data and uses late-bindingschema for searching the raw data, it enables a user to continueinvestigating and learn valuable insights about the raw data long afterdata ingestion time.

The ability to add multiple field definitions to the configuration fileat search time also results in increased flexibility. For example,multiple field definitions can be added to the configuration file tocapture the same field across events generated by different sourcetypes. This allows the data intake and query system to search andcorrelate data across heterogeneous sources flexibly and efficiently.

Further, by providing the field definitions for the queried fields atsearch time, the configuration file 712 allows the record data store tobe field searchable. In other words, the raw record data store can besearched using keywords as well as fields, wherein the fields aresearchable name/value pairings that distinguish one event from anotherand can be defined in configuration file 1402 using extraction rules. Incomparison to a search containing field names, a keyword search does notneed the configuration file and can search the event data directly asshown in FIG. 7B.

It should also be noted that any events filtered out by performing asearch-time field extraction using a configuration file can be furtherprocessed by directing the results of the filtering step to a processingstep using a pipelined search language. Using the prior example, a usercould pipeline the results of the compare step to an aggregate functionby asking the query search engine to count the number of events wherethe “clientip” field equals “127.0.0.1.”

As mentioned above, and as will be described in greater detail belowwith reference to, inter alia, FIGS. 18-49 , some functionality of thesearch head or indexers can be handled by different components of thesystem or removed altogether. For example, in some cases, the data isstored in a dataset source, which may be an indexer (or data storecontrolled by an indexer) or may be a different type of dataset source,such as a common storage or external data source. In addition, a querycoordinator or node can request events from the indexers or otherdataset source, apply extraction rules and correlate, automaticallydiscover certain custom fields, etc., as described above.

3.11. Example Search Screen

FIG. 8A is an interface diagram of an example user interface for asearch screen 800, in accordance with example embodiments. Search screen800 includes a search bar 802 that accepts user input in the form of asearch string. It also includes a time range picker 812 that enables theuser to specify a time range for the search. For historical searches(e.g., searches based on a particular historical time range), the usercan select a specific time range, or alternatively a relative timerange, such as “today,” “yesterday” or “last week.” For real-timesearches (e.g., searches whose results are based on data received inreal-time), the user can select the size of a time window to search forreal-time events. Search screen 800 also initially displays a “datasummary” dialog as is illustrated in FIG. 8B that enables the user toselect different sources for the events, such as by selecting specifichosts and log files.

After the search is executed, the search screen 800 in FIG. 8A candisplay the results through search results tabs 804, wherein searchresults tabs 804 includes: an “events tab” that displays variousinformation about events returned by the search; a “statistics tab” thatdisplays statistics about the search results; and a “visualization tab”that displays various visualizations of the search results. The eventstab illustrated in FIG. 8A displays a timeline graph 805 thatgraphically illustrates the number of events that occurred in one-hourintervals over the selected time range. The events tab also displays anevents list 808 that enables a user to view the machine data in each ofthe returned events.

The events tab additionally displays a sidebar that is an interactivefield picker 806. The field picker 806 may be displayed to a user inresponse to the search being executed and allows the user to furtheranalyze the search results based on the fields in the events of thesearch results. The field picker 806 includes field names that referencefields present in the events in the search results. The field picker maydisplay any Selected Fields 820 that a user has pre-selected for display(e.g., host, source, sourcetype) and may also display any InterestingFields 822 that the system determines may be interesting to the userbased on pre-specified criteria (e.g., action, bytes, categoryid,clientip, date_hour, date_mday, date_minute, etc.). The field pickeralso provides an option to display field names for all the fieldspresent in the events of the search results using the All Fields control824.

Each field name in the field picker 806 has a value type identifier tothe left of the field name, such as value type identifier 826. A valuetype identifier identifies the type of value for the respective field,such as an “a” for fields that include literal values or a “#” forfields that include numerical values.

Each field name in the field picker also has a unique value count to theright of the field name, such as unique value count 828. The uniquevalue count indicates the number of unique values for the respectivefield in the events of the search results.

Each field name is selectable to view the events in the search resultsthat have the field referenced by that field name. For example, a usercan select the “host” field name, and the events shown in the eventslist 808 will be updated with events in the search results that have thefield that is reference by the field name “host.”

3.12. Data Models

A data model is a hierarchically structured search-time mapping ofsemantic knowledge about one or more datasets. It encodes the domainknowledge used to build a variety of specialized searches of thosedatasets. Those searches, in turn, can be used to generate reports.

A data model is composed of one or more “objects” (or “data modelobjects”) that define or otherwise correspond to a specific set of data.An object is defined by constraints and attributes. An object'sconstraints are search criteria that define the set of events to beoperated on by running a search having that search criteria at the timethe data model is selected. An object's attributes are the set of fieldsto be exposed for operating on that set of events generated by thesearch criteria.

Objects in data models can be arranged hierarchically in parent/childrelationships. Each child object represents a subset of the datasetcovered by its parent object. The top-level objects in data models arecollectively referred to as “root objects.”

Child objects have inheritance. Child objects inherit constraints andattributes from their parent objects and may have additional constraintsand attributes of their own. Child objects provide a way of filteringevents from parent objects. Because a child object may provide anadditional constraint in addition to the constraints it has inheritedfrom its parent object, the dataset it represents may be a subset of thedataset that its parent represents. For example, a first data modelobject may define a broad set of data pertaining to e-mail activitygenerally, and another data model object may define specific datasetswithin the broad dataset, such as a subset of the e-mail data pertainingspecifically to e-mails sent. For example, a user can simply select an“e-mail activity” data model object to access a dataset relating toe-mails generally (e.g., sent or received), or select an “e-mails sent”data model object (or data sub-model object) to access a datasetrelating to e-mails sent.

Because a data model object is defined by its constraints (e.g., a setof search criteria) and attributes (e.g., a set of fields), a data modelobject can be used to quickly search data to identify a set of eventsand to identify a set of fields to be associated with the set of events.For example, an “e-mails sent” data model object may specify a searchfor events relating to e-mails that have been sent, and specify a set offields that are associated with the events. Thus, a user can retrieveand use the “e-mails sent” data model object to quickly search sourcedata for events relating to sent e-mails, and may be provided with alisting of the set of fields relevant to the events in a user interfacescreen.

Examples of data models can include electronic mail, authentication,databases, intrusion detection, malware, application state, alerts,compute inventory, network sessions, network traffic, performance,audits, updates, vulnerabilities, etc. Data models and their objects canbe designed by knowledge managers in an organization, and they canenable downstream users to quickly focus on a specific set of data. Auser iteratively applies a model development tool (not shown in FIG. 8A)to prepare a query that defines a subset of events and assigns an objectname to that subset. A child subset is created by further limiting aquery that generated a parent subset.

Data definitions in associated schemas can be taken from the commoninformation model (CIM) or can be devised for a particular schema andoptionally added to the CIM. Child objects inherit fields from parentsand can include fields not present in parents. A model developer canselect fewer extraction rules than are available for the sourcesreturned by the query that defines events belonging to a model.Selecting a limited set of extraction rules can be a tool forsimplifying and focusing the data model, while allowing a userflexibility to explore the data subset. Development of a data model isfurther explained in U.S. Pat. Nos. 8,788,525 and 8,788,526, bothentitled “DATA MODEL FOR MACHINE DATA FOR SEMANTIC SEARCH”, both issuedon 22 Jul. 2014, U.S. Pat. No. 8,983,994, entitled “GENERATION OF A DATAMODEL FOR SEARCHING MACHINE DATA”, issued on 17 Mar. 2015, U.S. Pat. No.9,128,980, entitled “GENERATION OF A DATA MODEL APPLIED TO QUERIES”,issued on 8 Sep. 2015, and U.S. Pat. No. 9,589,012, entitled “GENERATIONOF A DATA MODEL APPLIED TO OBJECT QUERIES”, issued on 7 Mar. 2017, eachof which is hereby incorporated by reference in its entirety for allpurposes.

A data model can also include reports. One or more report formats can beassociated with a particular data model and be made available to runagainst the data model. A user can use child objects to design reportswith object datasets that already have extraneous data pre-filtered out.In some embodiments, the data intake and query system 108 provides theuser with the ability to produce reports (e.g., a table, chart,visualization, etc.) without having to enter SPL, SQL, or other querylanguage terms into a search screen. Data models are used as the basisfor the search feature.

Data models may be selected in a report generation interface. The reportgenerator supports drag-and-drop organization of fields to be summarizedin a report. When a model is selected, the fields with availableextraction rules are made available for use in the report. The user mayrefine and/or filter search results to produce more precise reports. Theuser may select some fields for organizing the report and select otherfields for providing detail according to the report organization. Forexample, “region” and “salesperson” are fields used for organizing thereport and sales data can be summarized (subtotaled and totaled) withinthis organization. The report generator allows the user to specify oneor more fields within events and apply statistical analysis on valuesextracted from the specified one or more fields. The report generatormay aggregate search results across sets of events and generatestatistics based on aggregated search results. Building reports usingthe report generation interface is further explained in U.S. patentapplication Ser. No. 14/503,335, entitled “GENERATING REPORTS FROMUNSTRUCTURED DATA”, filed on 30 Sep. 2014, and which is herebyincorporated by reference in its entirety for all purposes. Datavisualizations also can be generated in a variety of formats, byreference to the data model. Reports, data visualizations, and datamodel objects can be saved and associated with the data model for futureuse. The data model object may be used to perform searches of otherdata.

FIGS. 9-15 are interface diagrams of example report generation userinterfaces, in accordance with example embodiments. The reportgeneration process may be driven by a predefined data model object, suchas a data model object defined and/or saved via a reporting applicationor a data model object obtained from another source. A user can load asaved data model object using a report editor. For example, the initialsearch query and fields used to drive the report editor may be obtainedfrom a data model object. The data model object that is used to drive areport generation process may define a search and a set of fields. Uponloading of the data model object, the report generation process mayenable a user to use the fields (e.g., the fields defined by the datamodel object) to define criteria for a report (e.g., filters, splitrows/columns, aggregates, etc.) and the search may be used to identifyevents (e.g., to identify events responsive to the search) used togenerate the report. That is, for example, if a data model object isselected to drive a report editor, the graphical user interface of thereport editor may enable a user to define reporting criteria for thereport using the fields associated with the selected data model object,and the events used to generate the report may be constrained to theevents that match, or otherwise satisfy, the search constraints of theselected data model object.

The selection of a data model object for use in driving a reportgeneration may be facilitated by a data model object selectioninterface. FIG. 9 illustrates an example interactive data modelselection graphical user interface 900 of a report editor that displaysa listing of available data models 901. The user may select one of thedata models 902.

FIG. 10 illustrates an example data model object selection graphicaluser interface 1000 that displays available data objects 1001 for theselected data object model 902. The user may select one of the displayeddata model objects 1002 for use in driving the report generationprocess.

Once a data model object is selected by the user, a user interfacescreen 1100 shown in FIG. 11A may display an interactive listing ofautomatic field identification options 1101 based on the selected datamodel object. For example, a user may select one of the threeillustrated options (e.g., the “All Fields” option 1102, the “SelectedFields” option 1103, or the “Coverage” option (e.g., fields with atleast a specified % of coverage) 1104). If the user selects the “AllFields” option 1102, all of the fields identified from the events thatwere returned in response to an initial search query may be selected.That is, for example, all of the fields of the identified data modelobject fields may be selected. If the user selects the “Selected Fields”option 1103, only the fields from the fields of the identified datamodel object fields that are selected by the user may be used. If theuser selects the “Coverage” option 1104, only the fields of theidentified data model object fields meeting a specified coveragecriteria may be selected. A percent coverage may refer to the percentageof events returned by the initial search query that a given fieldappears in. Thus, for example, if an object dataset includes 10,000events returned in response to an initial search query, and the“avg_age” field appears in 854 of those 10,000 events, then the“avg_age” field would have a coverage of 8.54% for that object dataset.If, for example, the user selects the “Coverage” option and specifies acoverage value of 2%, only fields having a coverage value equal to orgreater than 2% may be selected. The number of fields corresponding toeach selectable option may be displayed in association with each option.For example, “97” displayed next to the “All Fields” option 1102indicates that 97 fields will be selected if the “All Fields” option isselected. The “3” displayed next to the “Selected Fields” option 1103indicates that 3 of the 97 fields will be selected if the “SelectedFields” option is selected. The “49” displayed next to the “Coverage”option 1104 indicates that 49 of the 97 fields (e.g., the 49 fieldshaving a coverage of 2% or greater) will be selected if the “Coverage”option is selected. The number of fields corresponding to the “Coverage”option may be dynamically updated based on the specified percent ofcoverage.

FIG. 11B illustrates an example graphical user interface screen 1105displaying the reporting application's “Report Editor” page. The screenmay display interactive elements for defining various elements of areport. For example, the page includes a “Filters” element 1106, a“Split Rows” element 1107, a “Split Columns” element 1108, and a “ColumnValues” element 1109. The page may include a list of search results1111. In this example, the Split Rows element 1107 is expanded,revealing a listing of fields 1110 that can be used to define additionalcriteria (e.g., reporting criteria). The listing of fields 1110 maycorrespond to the selected fields. That is, the listing of fields 1110may list only the fields previously selected, either automaticallyand/or manually by a user. FIG. 11C illustrates a formatting dialogue1112 that may be displayed upon selecting a field from the listing offields 1110. The dialogue can be used to format the display of theresults of the selection (e.g., label the column for the selected fieldto be displayed as “component”).

FIG. 11D illustrates an example graphical user interface screen 1105including a table of results 1113 based on the selected criteriaincluding splitting the rows by the “component” field. A column 1114having an associated count for each component listed in the table may bedisplayed that indicates an aggregate count of the number of times thatthe particular field-value pair (e.g., the value in a row for aparticular field, such as the value “BucketMover” for the field“component”) occurs in the set of events responsive to the initialsearch query.

FIG. 12 illustrates an example graphical user interface screen 1200 thatallows the user to filter search results and to perform statisticalanalysis on values extracted from specific fields in the set of events.In this example, the top ten product names ranked by price are selectedas a filter 1201 that causes the display of the ten most popularproducts sorted by price. Each row is displayed by product name andprice 1202. This results in each product displayed in a column labeled“product name” along with an associated price in a column labeled“price” 1206. Statistical analysis of other fields in the eventsassociated with the ten most popular products have been specified ascolumn values 1203. A count of the number of successful purchases foreach product is displayed in column 1204. These statistics may beproduced by filtering the search results by the product name, findingall occurrences of a successful purchase in a field within the eventsand generating a total of the number of occurrences. A sum of the totalsales is displayed in column 1205, which is a result of themultiplication of the price and the number of successful purchases foreach product.

The reporting application allows the user to create graphicalvisualizations of the statistics generated for a report. For example,FIG. 13 illustrates an example graphical user interface 1300 thatdisplays a set of components and associated statistics 1301. Thereporting application allows the user to select a visualization of thestatistics in a graph (e.g., bar chart, scatter plot, area chart, linechart, pie chart, radial gauge, marker gauge, filler gauge, etc.), wherethe format of the graph may be selected using the user interfacecontrols 1302 along the left panel of the user interface 1300. FIG. 14illustrates an example of a bar chart visualization 1400 of an aspect ofthe statistical data 1301. FIG. 15 illustrates a scatter plotvisualization 1500 of an aspect of the statistical data 1301.

3.13. Acceleration Technique

The above-described system provides significant flexibility by enablinga user to analyze massive quantities of minimally-processed data “on thefly” at search time using a late-binding schema, instead of storingpre-specified portions of the data in a database at ingestion time. Thisflexibility enables a user to see valuable insights, correlate data, andperform subsequent queries to examine interesting aspects of the datathat may not have been apparent at ingestion time.

However, performing extraction and analysis operations at search timecan involve a large amount of data and require a large number ofcomputational operations, which can cause delays in processing thequeries. Advantageously, the data intake and query system also employs anumber of unique acceleration techniques that have been developed tospeed up analysis operations performed at search time. These techniquesinclude: (1) performing search operations in parallel across multipleindexers; (2) using a keyword index; (3) using a high performanceanalytics store; and (4) accelerating the process of generating reports.These novel techniques are described in more detail below. Althoughdescribed as being performed by an indexer, it will be understood thatvarious components can be used to perform similar functionality. Forexample, nodes can perform any one or any combination of the searchfunctions described herein. In some cases, the nodes perform the searchfunctions based on instructions received from a query coordinator.

3.13.1. Aggregation Technique

To facilitate faster query processing, a query can be structured suchthat multiple indexers perform the query in parallel, while aggregationof search results from the multiple indexers is performed locally at thesearch head. For example, FIG. 16 is an example search query receivedfrom a client and executed by search peers, in accordance with exampleembodiments. FIG. 16 illustrates how a search query 1602 received from aclient at a search head 210 can split into two phases, including: (1)subtasks 1604 (e.g., data retrieval or simple filtering) that may beperformed in parallel by indexers 206 for execution, and (2) a searchresults aggregation operation 1606 to be executed by the search headwhen the results are ultimately collected from the indexers.

During operation, upon receiving search query 1602, a search head 210determines that a portion of the operations involved with the searchquery may be performed locally by the search head. The search headmodifies search query 1602 by substituting “stats” (create aggregatestatistics over results sets received from the indexers at the searchhead) with “prestats” (create statistics by the indexer from localresults set) to produce search query 1604, and then distributes searchquery 1604 to distributed indexers, which are also referred to as“search peers” or “peer indexers.” Note that search queries maygenerally specify search criteria or operations to be performed onevents that meet the search criteria. Search queries may also specifyfield names, as well as search criteria for the values in the fields oroperations to be performed on the values in the fields.

Moreover, the search head may distribute the full search query to thesearch peers as illustrated in FIG. 6A, or may alternatively distributea modified version (e.g., a more restricted version) of the search queryto the search peers. In this example, the indexers are responsible forproducing the results and sending them to the search head. After theindexers return the results to the search head, the search headaggregates the received results 1606 to form a single search result set.By executing the query in this manner, the system effectivelydistributes the computational operations across the indexers whileminimizing data transfers.

As mentioned above, and as will be described in greater detail belowwith reference to, inter alia, 18-49, some functionality of the searchhead or indexers can be handled by different components of the system orremoved altogether. For example, in some cases, the data is stored inone or more dataset sources, such as, but not limited to an indexer (ordata store controlled by an indexer), common storage, external datasource, ingested data buffer, query acceleration data store, etc. Inaddition, in some cases a query coordinator can aggregate results frommultiple indexers or nodes, perform an aggregation operation 1606,determine what, if any, portion of the operations of the search queryare to be performed locally by the query coordinator, modify ortranslate a search query for an indexer or other dataset source,distribute the query to indexers, peers, or nodes, etc.

3.13.2. Keyword Index

As described above with reference to the flow charts in FIG. 5A, FIG.5B, and FIG. 6A, data intake and query system 108 can construct andmaintain one or more keyword indices to quickly identify eventscontaining specific keywords. This technique can greatly speed up theprocessing of queries involving specific keywords. As mentioned above,to build a keyword index, an indexer first identifies a set of keywords.Then, the indexer includes the identified keywords in an index, whichassociates each stored keyword with references to events containing thatkeyword, or to locations within events where that keyword is located.When an indexer subsequently receives a keyword-based query, the indexercan access the keyword index to quickly identify events containing thekeyword. In some embodiments, a node or other components of the systemthat performs search operations can use the keyword index to identifyevents, etc.

3.13.3. High Performance Analytics Store

To speed up certain types of queries, some embodiments of system 108create a high performance analytics store, which is referred to as a“summarization table,” that contains entries for specific field-valuepairs. Each of these entries keeps track of instances of a specificvalue in a specific field in the events and includes references toevents containing the specific value in the specific field. For example,an example entry in a summarization table can keep track of occurrencesof the value “94107” in a “ZIP code” field of a set of events and theentry includes references to all of the events that contain the value“94107” in the ZIP code field. This optimization technique enables thesystem to quickly process queries that seek to determine how many eventshave a particular value for a particular field. To this end, the systemcan examine the entry in the summarization table to count instances ofthe specific value in the field without having to go through theindividual events or perform data extractions at search time. Also, ifthe system needs to process all events that have a specific field-valuecombination, the system can use the references in the summarizationtable entry to directly access the events to extract further informationwithout having to search all of the events to find the specificfield-value combination at search time.

In some embodiments, the system maintains a separate summarization tablefor each of the above-described time-specific buckets that stores eventsfor a specific time range. A bucket-specific summarization tableincludes entries for specific field-value combinations that occur inevents in the specific bucket. Alternatively, the system can maintain aseparate summarization table for each indexer. The indexer-specificsummarization table includes entries for the events in a data store thatare managed by the specific indexer. Indexer-specific summarizationtables may also be bucket-specific.

The summarization table can be populated by running a periodic querythat scans a set of events to find instances of a specific field-valuecombination, or alternatively instances of all field-value combinationsfor a specific field. A periodic query can be initiated by a user, orcan be scheduled to occur automatically at specific time intervals. Aperiodic query can also be automatically launched in response to a querythat asks for a specific field-value combination.

In some cases, when the summarization tables may not cover all of theevents that are relevant to a query, the system can use thesummarization tables to obtain partial results for the events that arecovered by summarization tables, but may also have to search throughother events that are not covered by the summarization tables to produceadditional results. These additional results can then be combined withthe partial results to produce a final set of results for the query. Thesummarization table and associated techniques are described in moredetail in U.S. Pat. No. 8,682,925, entitled “DISTRIBUTED HIGHPERFORMANCE ANALYTICS STORE”, issued on 25 Mar. 2014, U.S. Pat. No.9,128,985, entitled “SUPPLEMENTING A HIGH PERFORMANCE ANALYTICS STOREWITH EVALUATION OF INDIVIDUAL EVENTS TO RESPOND TO AN EVENT QUERY”,issued on 8 Sep. 2015, and U.S. patent application Ser. No. 14/815,973,entitled “GENERATING AND STORING SUMMARIZATION TABLES FOR SETS OFSEARCHABLE EVENTS”, filed on 1 Aug. 2015, each of which is herebyincorporated by reference in its entirety for all purposes.

To speed up certain types of queries, e.g., frequently encounteredqueries or computationally intensive queries, some embodiments of system108 create a high performance analytics store, which is referred to as a“summarization table,” (also referred to as a “lexicon” or “invertedindex”) that contains entries for specific field-value pairs. Each ofthese entries keeps track of instances of a specific value in a specificfield in the event data and includes references to events containing thespecific value in the specific field. For example, an example entry inan inverted index can keep track of occurrences of the value “94107” ina “ZIP code” field of a set of events and the entry includes referencesto all of the events that contain the value “94107” in the ZIP codefield. Creating the inverted index data structure avoids needing toincur the computational overhead each time a statistical query needs tobe run on a frequently encountered field-value pair. In order toexpedite queries, in most embodiments, the search engine will employ theinverted index separate from the raw record data store to generateresponses to the received queries.

Note that the term “summarization table” or “inverted index” as usedherein is a data structure that may be generated by an indexer thatincludes at least field names and field values that have been extractedand/or indexed from event records. An inverted index may also includereference values that point to the location(s) in the field searchabledata store where the event records that include the field may be found.Also, an inverted index may be stored using well-known compressiontechniques to reduce its storage size.

Further, note that the term “reference value” (also referred to as a“posting value”) as used herein is a value that references the locationof a source record in the field searchable data store. In someembodiments, the reference value may include additional informationabout each record, such as timestamps, record size, meta-data, or thelike. Each reference value may be a unique identifier which may be usedto access the event data directly in the field searchable data store. Insome embodiments, the reference values may be ordered based on eachevent record's timestamp. For example, if numbers are used asidentifiers, they may be sorted so event records having a latertimestamp always have a lower valued identifier than event records withan earlier timestamp, or vice-versa. Reference values are often includedin inverted indexes for retrieving and/or identifying event records.

In one or more embodiments, an inverted index is generated in responseto a user-initiated collection query. The term “collection query” asused herein refers to queries that include commands that generatesummarization information and inverted indexes (or summarization tables)from event records stored in the field searchable data store.

Note that a collection query is a special type of query that can beuser-generated and is used to create an inverted index. A collectionquery is not the same as a query that is used to call up or invoke apre-existing inverted index. In one or more embodiment, a query cancomprise an initial step that calls up apre-generated inverted index onwhich further filtering and processing can be performed. For example,referring back to FIG. 13 , a set of events generated at block 1320 byeither using a “collection” query to create a new inverted index or bycalling up a pre-generated inverted index. A query with severalpipelined steps will start with a pre-generated index to accelerate thequery.

FIG. 7C illustrates the manner in which an inverted index is created andused in accordance with the disclosed embodiments. As shown in FIG. 7C,an inverted index 722 can be created in response to a user-initiatedcollection query using the event data 723 stored in the raw record datastore. For example, a non-limiting example of a collection query mayinclude “collect clientip=127.0.0.1” which may result in an invertedindex 722 being generated from the event data 723 as shown in FIG. 7C.Each entry in the inverted index 722 includes an event reference valuethat references the location of a source record in the field searchabledata store. The reference value may be used to access the original eventrecord directly from the field searchable data store.

In one or more embodiments, if one or more of the queries is acollection query, the responsive indexers may generate summarizationinformation based on the fields of the event records located in thefield searchable data store. In at least one of the various embodiments,one or more of the fields used in the summarization information may belisted in the collection query and/or they may be determined based onterms included in the collection query. For example, a collection querymay include an explicit list of fields to summarize. Or, in at least oneof the various embodiments, a collection query may include terms orexpressions that explicitly define the fields, e.g., using regex rules.In FIG. 7C, prior to running the collection query that generates theinverted index 722, the field name “clientip” may need to be defined ina configuration file by specifying the “access_combined” source type anda regular expression rule to parse out the client IP address.Alternatively, the collection query may contain an explicit definitionfor the field name “clientip” which may obviate the need to referencethe configuration file at search time.

In one or more embodiments, collection queries may be saved andscheduled to run periodically. These scheduled collection queries mayperiodically update the summarization information corresponding to thequery. For example, if the collection query that generates invertedindex 722 is scheduled to run periodically, one or more indexers wouldperiodically search through the relevant buckets to update invertedindex 722 with event data for any new events with the “clientip” valueof “127.0.0.1.”

In some embodiments, the inverted indexes that include fields, values,and reference value (e.g., inverted index 722) for event records may beincluded in the summarization information provided to the user. In otherembodiments, a user may not be interested in specific fields and valuescontained in the inverted index, but may need to perform a statisticalquery on the data in the inverted index. For example, referencing theexample of FIG. 7C rather than viewing the fields within inverted index722, a user may want to generate a count of all client requests from IPaddress “127.0.0.1.” In this case, the search engine would simply returna result of “4” rather than including details about the inverted index722 in the information provided to the user.

The pipelined search language, e.g., SPL of the SPLUNK® ENTERPRISEsystem can be used to pipe the contents of an inverted index to astatistical query using the “stats” command for example. A “stats” queryrefers to queries that generate result sets that may produce aggregateand statistical results from event records, e.g., average, mean, max,min, rms, etc. Where sufficient information is available in an invertedindex, a “stats” query may generate their result sets rapidly from thesummarization information available in the inverted index rather thandirectly scanning event records. For example, the contents of invertedindex 722 can be pipelined to a stats query, e.g., a “count” functionthat counts the number of entries in the inverted index and returns avalue of “4.” In this way, inverted indexes may enable various statsqueries to be performed absent scanning or search the event records.Accordingly, this optimization technique enables the system to quicklyprocess queries that seek to determine how many events have a particularvalue for a particular field. To this end, the system can examine theentry in the inverted index 722 to count instances of the specific valuein the field without having to go through the individual events orperform data extractions at search time.

In some embodiments, the system maintains a separate inverted index foreach of the above-described time-specific buckets that stores events fora specific time range. A bucket-specific inverted index includes entriesfor specific field-value combinations that occur in events in thespecific bucket. Alternatively, the system can maintain a separateinverted index for each indexer. The indexer-specific inverted indexincludes entries for the events in a data store that are managed by thespecific indexer. Indexer-specific inverted indexes may also bebucket-specific. In at least one or more embodiments, if one or more ofthe queries is a stats query, each indexer may generate a partial resultset from previously generated summarization information. The partialresult sets may be returned to the search head that received the queryand combined into a single result set for the query

As mentioned above, the inverted index can be populated by running aperiodic query that scans a set of events to find instances of aspecific field-value combination, or alternatively instances of allfield-value combinations for a specific field. A periodic query can beinitiated by a user, or can be scheduled to occur automatically atspecific time intervals. A periodic query can also be automaticallylaunched in response to a query that asks for a specific field-valuecombination. In some embodiments, if summarization information is absentfrom an indexer that includes responsive event records, further actionsmay be taken, such as, the summarization information may generated onthe fly, warnings may be provided the user, the collection queryoperation may be halted, the absence of summarization information may beignored, or the like, or combination thereof.

In one or more embodiments, an inverted index may be set up to updatecontinually. For example, the query may ask for the inverted index toupdate its result periodically, e.g., every hour. In such instances, theinverted index may be a dynamic data structure that is regularly updatedto include information regarding incoming events.

In some cases, e.g., where a query is executed before an inverted indexupdates, when the inverted index may not cover all of the events thatare relevant to a query, the system can use the inverted index to obtainpartial results for the events that are covered by inverted index, butmay also have to search through other events that are not covered by theinverted index to produce additional results on the fly. In other words,an indexer would need to search through event data on the data store tosupplement the partial results. These additional results can then becombined with the partial results to produce a final set of results forthe query. Note that in typical instances where an inverted index is notcompletely up to date, the number of events that an indexer would needto search through to supplement the results from the inverted indexwould be relatively small. In other words, the search to get the mostrecent results can be quick and efficient because only a small number ofevent records will be searched through to supplement the informationfrom the inverted index. The inverted index and associated techniquesare described in more detail in U.S. Pat. No. 8,682,925, entitled“DISTRIBUTED HIGH PERFORMANCE ANALYTICS STORE”, issued on 25 Mar. 2014,U.S. Pat. No. 9,128,985, entitled “SUPPLEMENTING A HIGH PERFORMANCEANALYTICS STORE WITH EVALUATION OF INDIVIDUAL EVENTS TO RESPOND TO ANEVENT QUERY”, filed on 31 Jan. 2014, and U.S. patent application Ser.No. 14/815,973, entitled “STORAGE MEDIUM AND CONTROL DEVICE”, filed on21 Feb. 2014, each of which is hereby incorporated by reference in itsentirety. In some cases, the inverted indexes can be made available, aspart of a common storage, to nodes or other components of the systemthat perform search operations.

3.13.4. Extracting Event Data Using Posting

In one or more embodiments, if the system needs to process all eventsthat have a specific field-value combination, the system can use thereferences in the inverted index entry to directly access the events toextract further information without having to search all of the eventsto find the specific field-value combination at search time. In otherwords, the system can use the reference values to locate the associatedevent data in the field searchable data store and extract furtherinformation from those events, e.g., extract further field values fromthe events for purposes of filtering or processing or both.

The information extracted from the event data using the reference valuescan be directed for further filtering or processing in a query using thepipeline search language. The pipelined search language will, in oneembodiment, include syntax that can direct the initial filtering step ina query to an inverted index. In one embodiment, a user would includesyntax in the query that explicitly directs the initial searching orfiltering step to the inverted index.

Referencing the example in FIG. 7C, if the user determines that sheneeds the user id fields associated with the client requests from IPaddress “127.0.0.1,” instead of incurring the computational overhead ofperforming a brand new search or re-generating the inverted index withan additional field, the user can generate a query that explicitlydirects or pipes the contents of the already generated inverted index722 to another filtering step requesting the user ids for the entries ininverted index 722 where the server response time is greater than“0.0900” microseconds. The search engine would use the reference valuesstored in inverted index 722 to retrieve the event data from the fieldsearchable data store, filter the results based on the “response time”field values and, further, extract the user id field from the resultingevent data to return to the user. In the present instance, the user ids“frank” and “matt” would be returned to the user from the generatedresults table 725.

In one embodiment, the same methodology can be used to pipe the contentsof the inverted index to a processing step. In other words, the user isable to use the inverted index 722 to efficiently and quickly performaggregate functions on field values that were not part of the initiallygenerated inverted index. For example, a user may want to determine anaverage object size (size of the requested gif) requested by clientsfrom IP address “127.0.0.1.” In this case, the search engine would againuse the reference values stored in inverted index 722 to retrieve theevent data from the field searchable data store and, further, extractthe object size field values from the associated events 731, 732, 733and 734. Once, the corresponding object sizes have been extracted (e.g.,2326, 2900, 2920, and 5000), the average can be computed and returned tothe user.

In one embodiment, instead of explicitly invoking the inverted index ina user-generated query, e.g., by the use of special commands or syntax,the SPLUNK® ENTERPRISE system can be configured to automaticallydetermine if any prior-generated inverted index can be used to expeditea user query. For example, the user's query may request the averageobject size (size of the requested gif) requested by clients from IPaddress “127.0.0.1.” without any reference to or use of inverted index722. The search engine, in this case, would automatically determine thatan inverted index 722 already exists in the system that could expeditethis query. In one embodiment, prior to running any search comprising afield-value pair, for example, a search engine may search though all theexisting inverted indexes to determine if a pre-generated inverted indexcould be used to expedite the search comprising the field-value pair.Accordingly, the search engine would automatically use the pre-generatedinverted index, e.g., inverted index 722 to generate the results 725without any user-involvement that directs the use of the inverted index.

Using the reference values in an inverted index to be able to directlyaccess the event data in the field searchable data store and extractfurther information from the associated event data for further filteringand processing is highly advantageous because it avoids incurring thecomputation overhead of regenerating the inverted index with additionalfields or performing a new search.

The data intake and query system includes one or more forwarders thatreceive raw machine data from a variety of input data sources, and oneor more indexers that process and store the data in one or more datastores. By distributing events among the indexers and data stores, theindexers can analyze events for a query in parallel. In one or moreembodiments, a multiple indexer implementation of the search systemwould maintain a separate and respective inverted index for each of theabove-described time-specific buckets that stores events for a specifictime range. A bucket-specific inverted index includes entries forspecific field-value combinations that occur in events in the specificbucket. As explained above, a search head would be able to correlate andsynthesize data from across the various buckets and indexers.

This feature advantageously expedites searches because instead ofperforming a computationally intensive search in a centrally locatedinverted index that catalogues all the relevant events, an indexer isable to directly search an inverted index stored in a bucket associatedwith the time-range specified in the query. This allows the search to beperformed in parallel across the various indexers. Further, if the queryrequests further filtering or processing to be conducted on the eventdata referenced by the locally stored bucket-specific inverted index,the indexer is able to simply access the event records stored in theassociated bucket for further filtering and processing instead ofneeding to access a central repository of event records, which woulddramatically add to the computational overhead.

In one embodiment, there may be multiple buckets associated with thetime-range specified in a query. If the query is directed to an invertedindex, or if the search engine automatically determines that using aninverted index would expedite the processing of the query, the indexerswill search through each of the inverted indexes associated with thebuckets for the specified time-range. This feature allows the HighPerformance Analytics Store to be scaled easily.

In certain instances, where a query is executed before a bucket-specificinverted index updates, when the bucket-specific inverted index may notcover all of the events that are relevant to a query, the system can usethe bucket-specific inverted index to obtain partial results for theevents that are covered by bucket-specific inverted index, but may alsohave to search through the event data in the bucket associated with thebucket-specific inverted index to produce additional results on the fly.In other words, an indexer would need to search through event datastored in the bucket (that was not yet processed by the indexer for thecorresponding inverted index) to supplement the partial results from thebucket-specific inverted index.

FIG. 7D presents a flowchart illustrating how an inverted index in apipelined search query can be used to determine a set of event data thatcan be further limited by filtering or processing in accordance with thedisclosed embodiments.

At block 742, a query is received by a data intake and query system. Insome embodiments, the query can be receive as a user generated queryentered into search bar of a graphical user search interface. The searchinterface also includes a time range control element that enablesspecification of a time range for the query.

At block 744, an inverted index is retrieved. Note, that the invertedindex can be retrieved in response to an explicit user search commandinputted as part of the user generated query. Alternatively, the searchengine can be configured to automatically use an inverted index if itdetermines that using the inverted index would expedite the servicing ofthe user generated query. Each of the entries in an inverted index keepstrack of instances of a specific value in a specific field in the eventdata and includes references to events containing the specific value inthe specific field. In order to expedite queries, in most embodiments,the search engine will employ the inverted index separate from the rawrecord data store to generate responses to the received queries.

At block 746, the query engine determines if the query contains furtherfiltering and processing steps. If the query contains no furthercommands, then, in one embodiment, summarization information can beprovided to the user at block 754.

If, however, the query does contain further filtering and processingcommands, then at block 750, the query engine determines if the commandsrelate to further filtering or processing of the data extracted as partof the inverted index or whether the commands are directed to using theinverted index as an initial filtering step to further filter andprocess event data referenced by the entries in the inverted index. Ifthe query can be completed using data already in the generated invertedindex, then the further filtering or processing steps, e.g., a “count”number of records function, “average” number of records per hour etc.are performed and the results are provided to the user at block 752.

If, however, the query references fields that are not extracted in theinverted index, then the indexers will access event data pointed to bythe reference values in the inverted index to retrieve any furtherinformation required at block 756. Subsequently, any further filteringor processing steps are performed on the fields extracted directly fromthe event data and the results are provided to the user at step 758.

As described throughout, it will be understood that although describedas being performed by an indexer, these functions can be performed byanother component of the system, such as a query coordinator or node.For example, nodes can use inverted indexes to identify relevant data,etc. The inverted indexes can be stored with buckets in a commonstorage, etc.

3.13.5. Accelerating Report Generation

In some embodiments, a data server system such as the data intake andquery system can accelerate the process of periodically generatingupdated reports based on query results. To accelerate this process, asummarization engine automatically examines the query to determinewhether generation of updated reports can be accelerated by creatingintermediate summaries. If reports can be accelerated, the summarizationengine periodically generates a summary covering data obtained during alatest non-overlapping time period. For example, where the query seeksevents meeting a specified criteria, a summary for the time periodincludes only events within the time period that meet the specifiedcriteria. Similarly, if the query seeks statistics calculated from theevents, such as the number of events that match the specified criteria,then the summary for the time period includes the number of events inthe period that match the specified criteria.

In addition to the creation of the summaries, the summarization engineschedules the periodic updating of the report associated with the query.During each scheduled report update, the query engine determines whetherintermediate summaries have been generated covering portions of the timeperiod covered by the report update. If so, then the report is generatedbased on the information contained in the summaries. Also, if additionalevent data has been received and has not yet been summarized, and isrequired to generate the complete report, the query can be run on theseadditional events. Then, the results returned by this query on theadditional events, along with the partial results obtained from theintermediate summaries, can be combined to generate the updated report.This process is repeated each time the report is updated. Alternatively,if the system stores events in buckets covering specific time ranges,then the summaries can be generated on a bucket-by-bucket basis. Notethat producing intermediate summaries can save the work involved inre-running the query for previous time periods, so advantageously onlythe newer events needs to be processed while generating an updatedreport. These report acceleration techniques are described in moredetail in U.S. Pat. No. 8,589,403, entitled “COMPRESSED JOURNALING INEVENT TRACKING FILES FOR METADATA RECOVERY AND REPLICATION”, issued on19 Nov. 2013, U.S. Pat. No. 8,412,696, entitled “REAL TIME SEARCHING ANDREPORTING”, issued on 2 Apr. 2011, and U.S. Pat. Nos. 8,589,375 and8,589,432, both also entitled “REAL TIME SEARCHING AND REPORTING”, bothissued on 19 Nov. 2013, each of which is hereby incorporated byreference in its entirety for all purposes.

3.14. Security Features

The data intake and query system provides various schemas, dashboards,and visualizations that simplify developers' tasks to createapplications with additional capabilities. One such application is thean enterprise security application, such as SPLUNK® ENTERPRISE SECURITY,which performs monitoring and alerting operations and includes analyticsto facilitate identifying both known and unknown security threats basedon large volumes of data stored by the data intake and query system. Theenterprise security application provides the security practitioner withvisibility into security-relevant threats found in the enterpriseinfrastructure by capturing, monitoring, and reporting on data fromenterprise security devices, systems, and applications. Through the useof the data intake and query system searching and reportingcapabilities, the enterprise security application provides a top-downand bottom-up view of an organization's security posture.

The enterprise security application leverages the data intake and querysystem search-time normalization techniques, saved searches, andcorrelation searches to provide visibility into security-relevantthreats and activity and generate notable events for tracking. Theenterprise security application enables the security practitioner toinvestigate and explore the data to find new or unknown threats that donot follow signature-based patterns.

Conventional Security Information and Event Management (SIEM) systemslack the infrastructure to effectively store and analyze large volumesof security-related data. Traditional SIEM systems typically use fixedschemas to extract data from pre-defined security-related fields at dataingestion time and store the extracted data in a relational database.This traditional data extraction process (and associated reduction indata size) that occurs at data ingestion time inevitably hampers futureincident investigations that may need original data to determine theroot cause of a security issue, or to detect the onset of an impendingsecurity threat.

In contrast, the enterprise security application system stores largevolumes of minimally-processed security-related data at ingestion timefor later retrieval and analysis at search time when a live securitythreat is being investigated. To facilitate this data retrieval process,the enterprise security application provides pre-specified schemas forextracting relevant values from the different types of security-relatedevents and enables a user to define such schemas.

The enterprise security application can process many types ofsecurity-related information. In general, this security-relatedinformation can include any information that can be used to identifysecurity threats. For example, the security-related information caninclude network-related information, such as IP addresses, domain names,asset identifiers, network traffic volume, uniform resource locatorstrings, and source addresses. The process of detecting security threatsfor network-related information is further described in U.S. Pat. No.8,826,434, entitled “SECURITY THREAT DETECTION BASED ON INDICATIONS INBIG DATA OF ACCESS TO NEWLY REGISTERED DOMAINS”, issued on 2 Sep. 2014,U.S. Pat. No. 9,215,240, entitled “INVESTIGATIVE AND DYNAMIC DETECTIONOF POTENTIAL SECURITY-THREAT INDICATORS FROM EVENTS IN BIG DATA”, issuedon 15 Dec. 2015, U.S. Pat. No. 9,173,801, entitled “GRAPHIC DISPLAY OFSECURITY THREATS BASED ON INDICATIONS OF ACCESS TO NEWLY REGISTEREDDOMAINS”, issued on 3 Nov. 2015, U.S. Pat. No. 9,248,068, entitled“SECURITY THREAT DETECTION OF NEWLY REGISTERED DOMAINS”, issued on 2Feb. 2016, U.S. Pat. No. 9,426,172, entitled “SECURITY THREAT DETECTIONUSING DOMAIN NAME ACCESSES”, issued on 23 Aug. 2016, and U.S. Pat. No.9,432,396, entitled “SECURITY THREAT DETECTION USING DOMAIN NAMEREGISTRATIONS”, issued on 30 Aug. 2016, each of which is herebyincorporated by reference in its entirety for all purposes.Security-related information can also include malware infection data andsystem configuration information, as well as access control information,such as login/logout information and access failure notifications. Thesecurity-related information can originate from various sources within adata center, such as hosts, virtual machines, storage devices andsensors. The security-related information can also originate fromvarious sources in a network, such as routers, switches, email servers,proxy servers, gateways, firewalls and intrusion-detection systems.

During operation, the enterprise security application facilitatesdetecting “notable events” that are likely to indicate a securitythreat. A notable event represents one or more anomalous incidents, theoccurrence of which can be identified based on one or more events (e.g.,time stamped portions of raw machine data) fulfilling pre-specifiedand/or dynamically-determined (e.g., based on machine-learning) criteriadefined for that notable event. Examples of notable events include therepeated occurrence of an abnormal spike in network usage over a periodof time, a single occurrence of unauthorized access to system, a hostcommunicating with a server on a known threat list, and the like. Thesenotable events can be detected in a number of ways, such as: (1) a usercan notice a correlation in events and can manually identify that acorresponding group of one or more events amounts to a notable event; or(2) a user can define a “correlation search” specifying criteria for anotable event, and every time one or more events satisfy the criteria,the application can indicate that the one or more events correspond to anotable event; and the like. A user can alternatively select apre-defined correlation search provided by the application. Note thatcorrelation searches can be run continuously or at regular intervals(e.g., every hour) to search for notable events. Upon detection, notableevents can be stored in a dedicated “notable events index,” which can besubsequently accessed to generate various visualizations containingsecurity-related information. Also, alerts can be generated to notifysystem operators when important notable events are discovered.

The enterprise security application provides various visualizations toaid in discovering security threats, such as a “key indicators view”that enables a user to view security metrics, such as counts ofdifferent types of notable events. For example, FIG. 17A illustrates anexample key indicators view 1700 that comprises a dashboard, which candisplay a value 1701, for various security-related metrics, such asmalware infections 1702. It can also display a change in a metric value1703, which indicates that the number of malware infections increased by63 during the preceding interval. Key indicators view 1700 additionallydisplays a histogram panel 1704 that displays a histogram of notableevents organized by urgency values, and a histogram of notable eventsorganized by time intervals. This key indicators view is described infurther detail in pending U.S. patent application Ser. No. 13/956,338,entitled “KEY INDICATORS VIEW”, filed on 31 Jul. 2013, and which ishereby incorporated by reference in its entirety for all purposes.

These visualizations can also include an “incident review dashboard”that enables a user to view and act on “notable events.” These notableevents can include: (1) a single event of high importance, such as anyactivity from a known web attacker; or (2) multiple events thatcollectively warrant review, such as a large number of authenticationfailures on a host followed by a successful authentication. For example,FIG. 17B illustrates an example incident review dashboard 1710 thatincludes a set of incident attribute fields 1711 that, for example,enables a user to specify a time range field 1712 for the displayedevents. It also includes a timeline 1713 that graphically illustratesthe number of incidents that occurred in time intervals over theselected time range. It additionally displays an events list 1714 thatenables a user to view a list of all of the notable events that matchthe criteria in the incident attributes fields 1711. To facilitateidentifying patterns among the notable events, each notable event can beassociated with an urgency value (e.g., low, medium, high, critical),which is indicated in the incident review dashboard. The urgency valuefor a detected event can be determined based on the severity of theevent and the priority of the system component associated with theevent.

3.15. Data Center Monitoring

As mentioned above, the data intake and query platform provides variousfeatures that simplify the developers' task to create variousapplications. One such application is a virtual machine monitoringapplication, such as SPLUNK® APP FOR VMWARE® that provides operationalvisibility into granular performance metrics, logs, tasks and events,and topology from hosts, virtual machines and virtual centers. Itempowers administrators with an accurate real-time picture of the healthof the environment, proactively identifying performance and capacitybottlenecks.

Conventional data-center-monitoring systems lack the infrastructure toeffectively store and analyze large volumes of machine-generated data,such as performance information and log data obtained from the datacenter. In conventional data-center-monitoring systems,machine-generated data is typically pre-processed prior to being stored,for example, by extracting pre-specified data items and storing them ina database to facilitate subsequent retrieval and analysis at searchtime. However, the rest of the data is not saved and discarded duringpre-processing.

In contrast, the virtual machine monitoring application stores largevolumes of minimally processed machine data, such as performanceinformation and log data, at ingestion time for later retrieval andanalysis at search time when a live performance issue is beinginvestigated. In addition to data obtained from various log files, thisperformance-related information can include values for performancemetrics obtained through an application programming interface (API)provided as part of the vSphere Hypervisor™ system distributed byVMware, Inc. of Palo Alto, Calif. For example, these performance metricscan include: (1) CPU-related performance metrics; (2) disk-relatedperformance metrics; (3) memory-related performance metrics; (4)network-related performance metrics; (5) energy-usage statistics; (6)data-traffic-related performance metrics; (7) overall systemavailability performance metrics; (8) cluster-related performancemetrics; and (9) virtual machine performance statistics. Suchperformance metrics are described in U.S. patent application Ser. No.14/167,316, entitled “CORRELATION FOR USER-SELECTED TIME RANGES OFVALUES FOR PERFORMANCE METRICS OF COMPONENTS IN ANINFORMATION-TECHNOLOGY ENVIRONMENT WITH LOG DATA FROM THATINFORMATION-TECHNOLOGY ENVIRONMENT”, filed on 29 Jan. 2014, and which ishereby incorporated by reference in its entirety for all purposes.

To facilitate retrieving information of interest from performance dataand log files, the virtual machine monitoring application providespre-specified schemas for extracting relevant values from differenttypes of performance-related events, and also enables a user to definesuch schemas.

The virtual machine monitoring application additionally provides variousvisualizations to facilitate detecting and diagnosing the root cause ofperformance problems. For example, one such visualization is a“proactive monitoring tree” that enables a user to easily view andunderstand relationships among various factors that affect theperformance of a hierarchically structured computing system. Thisproactive monitoring tree enables a user to easily navigate thehierarchy by selectively expanding nodes representing various entities(e.g., virtual centers or computing clusters) to view performanceinformation for lower-level nodes associated with lower-level entities(e.g., virtual machines or host systems). Example node-expansionoperations are illustrated in FIG. 17C, wherein nodes 1733 and 1734 areselectively expanded. Note that nodes 1731-1739 can be displayed usingdifferent patterns or colors to represent different performance states,such as a critical state, a warning state, a normal state or anunknown/offline state. The ease of navigation provided by selectiveexpansion in combination with the associated performance-stateinformation enables a user to quickly diagnose the root cause of aperformance problem. The proactive monitoring tree is described infurther detail in U.S. Pat. No. 9,185,007, entitled “PROACTIVEMONITORING TREE WITH SEVERITY STATE SORTING”, issued on 10 Nov. 2015,and U.S. Pat. No. 9,426,045, also entitled “PROACTIVE MONITORING TREEWITH SEVERITY STATE SORTING”, issued on 23 Aug. 2016, each of which ishereby incorporated by reference in its entirety for all purposes.

The virtual machine monitoring application also provides a userinterface that enables a user to select a specific time range and thenview heterogeneous data comprising events, log data, and associatedperformance metrics for the selected time range. For example, the screenillustrated in FIG. 17D displays a listing of recent “tasks and events”and a listing of recent “log entries” for a selected time range above aperformance-metric graph for “average CPU core utilization” for theselected time range. Note that a user is able to operate pull-down menus1742 to selectively display different performance metric graphs for theselected time range. This enables the user to correlate trends in theperformance-metric graph with corresponding event and log data toquickly determine the root cause of a performance problem. This userinterface is described in more detail in U.S. patent application Ser.No. 14/167,316, entitled “CORRELATION FOR USER-SELECTED TIME RANGES OFVALUES FOR PERFORMANCE METRICS OF COMPONENTS IN ANINFORMATION-TECHNOLOGY ENVIRONMENT WITH LOG DATA FROM THATINFORMATION-TECHNOLOGY ENVIRONMENT”, filed on 29 Jan. 2014, and which ishereby incorporated by reference in its entirety for all purposes.

3.16. IT Service Monitoring

As previously mentioned, the data intake and query platform providesvarious schemas, dashboards and visualizations that make it easy fordevelopers to create applications to provide additional capabilities.One such application is an IT monitoring application, such as SPLUNK® ITSERVICE INTELLIGENCE™, which performs monitoring and alertingoperations. The IT monitoring application also includes analytics tohelp an analyst diagnose the root cause of performance problems based onlarge volumes of data stored by the data intake and query system ascorrelated to the various services an IT organization provides (aservice-centric view). This differs significantly from conventional ITmonitoring systems that lack the infrastructure to effectively store andanalyze large volumes of service-related events. Traditional servicemonitoring systems typically use fixed schemas to extract data frompre-defined fields at data ingestion time, wherein the extracted data istypically stored in a relational database. This data extraction processand associated reduction in data content that occurs at data ingestiontime inevitably hampers future investigations, when all of the originaldata may be needed to determine the root cause of or contributingfactors to a service issue.

In contrast, an IT monitoring application system stores large volumes ofminimally-processed service-related data at ingestion time for laterretrieval and analysis at search time, to perform regular monitoring, orto investigate a service issue. To facilitate this data retrievalprocess, the IT monitoring application enables a user to define an IToperations infrastructure from the perspective of the services itprovides. In this service-centric approach, a service such as corporatee-mail may be defined in terms of the entities employed to provide theservice, such as host machines and network devices. Each entity isdefined to include information for identifying all of the events thatpertains to the entity, whether produced by the entity itself or byanother machine, and considering the many various ways the entity may beidentified in machine data (such as by a URL, an IP address, or machinename). The service and entity definitions can organize events around aservice so that all of the events pertaining to that service can beeasily identified. This capability provides a foundation for theimplementation of Key Performance Indicators.

One or more Key Performance Indicators (KPI's) are defined for a servicewithin the IT monitoring application. Each KPI measures an aspect ofservice performance at a point in time or over a period of time (aspectKPI's). Each KPI is defined by a search query that derives a KPI valuefrom the machine data of events associated with the entities thatprovide the service. Information in the entity definitions may be usedto identify the appropriate events at the time a KPI is defined orwhenever a KPI value is being determined. The KPI values derived overtime may be stored to build a valuable repository of current andhistorical performance information for the service, and the repository,itself, may be subject to search query processing. Aggregate KPIs may bedefined to provide a measure of service performance calculated from aset of service aspect KPI values; this aggregate may even be takenacross defined timeframes and/or across multiple services. A particularservice may have an aggregate KPI derived from substantially all of theaspect KPI's of the service to indicate an overall health score for theservice.

The IT monitoring application facilitates the production of meaningfulaggregate KPI's through a system of KPI thresholds and state values.Different KPI definitions may produce values in different ranges, and sothe same value may mean something very different from one KPI definitionto another. To address this, the IT monitoring application implements atranslation of individual KPI values to a common domain of “state”values. For example, a KPI range of values may be 1-100, or 50-275,while values in the state domain may be ‘critical,’ ‘warning,’ ‘normal,’and ‘informational’. Thresholds associated with a particular KPIdefinition determine ranges of values for that KPI that correspond tothe various state values. In one case, KPI values 95-100 may be set tocorrespond to ‘critical’ in the state domain. KPI values from disparateKPI's can be processed uniformly once they are translated into thecommon state values using the thresholds. For example, “normal 80% ofthe time” can be applied across various KPI's. To provide meaningfulaggregate KPI's, a weighting value can be assigned to each KPI so thatits influence on the calculated aggregate KPI value is increased ordecreased relative to the other KPI's.

One service in an IT environment often impacts, or is impacted by,another service. The IT monitoring application can reflect thesedependencies. For example, a dependency relationship between a corporatee-mail service and a centralized authentication service can be reflectedby recording an association between their respective servicedefinitions. The recorded associations establish a service dependencytopology that informs the data or selection options presented in a GUI,for example. (The service dependency topology is like a “map” showinghow services are connected based on their dependencies.) The servicetopology may itself be depicted in a GUI and may be interactive to allownavigation among related services.

Entity definitions in the IT monitoring application can includeinformational fields that can serve as metadata, implied data fields, orattributed data fields for the events identified by other aspects of theentity definition. Entity definitions in the IT monitoring applicationcan also be created and updated by an import of tabular data (asrepresented in a CSV, another delimited file, or a search query resultset). The import may be GUI-mediated or processed using importparameters from a GUI-based import definition process. Entitydefinitions in the IT monitoring application can also be associated witha service by means of a service definition rule. Processing the ruleresults in the matching entity definitions being associated with theservice definition. The rule can be processed at creation time, andthereafter on a scheduled or on-demand basis. This allows dynamic,rule-based updates to the service definition.

During operation, the IT monitoring application can recognize notableevents that may indicate a service performance problem or othersituation of interest. These notable events can be recognized by a“correlation search” specifying trigger criteria for a notable event:every time KPI values satisfy the criteria, the application indicates anotable event. A severity level for the notable event may also bespecified. Furthermore, when trigger criteria are satisfied, thecorrelation search may additionally or alternatively cause a serviceticket to be created in an IT service management (ITSM) system, such asa systems available from ServiceNow, Inc., of Santa Clara, Calif.

SPLUNK® IT SERVICE INTELLIGENCE™ provides various visualizations builton its service-centric organization of events and the KPI valuesgenerated and collected. Visualizations can be particularly useful formonitoring or investigating service performance. The IT monitoringapplication provides a service monitoring interface suitable as the homepage for ongoing IT service monitoring. The interface is appropriate forsettings such as desktop use or for a wall-mounted display in a networkoperations center (NOC). The interface may prominently display aservices health section with tiles for the aggregate KPI's indicatingoverall health for defined services and a general KPI section with tilesfor KPI's related to individual service aspects. These tiles may displayKPI information in a variety of ways, such as by being colored andordered according to factors like the KPI state value. They also can beinteractive and navigate to visualizations of more detailed KPIinformation.

The IT monitoring application provides a service-monitoring dashboardvisualization based on a user-defined template. The template can includeuser-selectable widgets of varying types and styles to display KPIinformation. The content and the appearance of widgets can responddynamically to changing KPI information. The KPI widgets can appear inconjunction with a background image, user drawing objects, or othervisual elements, that depict the IT operations environment, for example.The KPI widgets or other GUI elements can be interactive so as toprovide navigation to visualizations of more detailed KPI information.

The IT monitoring application provides a visualization showing detailedtime-series information for multiple KPI's in parallel graph lanes. Thelength of each lane can correspond to a uniform time range, while thewidth of each lane may be automatically adjusted to fit the displayedKPI data. Data within each lane may be displayed in a user selectablestyle, such as a line, area, or bar chart. During operation a user mayselect a position in the time range of the graph lanes to activate laneinspection at that point in time. Lane inspection may display anindicator for the selected time across the graph lanes and display theKPI value associated with that point in time for each of the graphlanes. The visualization may also provide navigation to an interface fordefining a correlation search, using information from the visualizationto pre-populate the definition.

The IT monitoring application provides a visualization for incidentreview showing detailed information for notable events. The incidentreview visualization may also show summary information for the notableevents over a time frame, such as an indication of the number of notableevents at each of a number of severity levels. The severity leveldisplay may be presented as a rainbow chart with the warmest colorassociated with the highest severity classification. The incident reviewvisualization may also show summary information for the notable eventsover a time frame, such as the number of notable events occurring withinsegments of the time frame. The incident review visualization maydisplay a list of notable events within the time frame ordered by anynumber of factors, such as time or severity. The selection of aparticular notable event from the list may display detailed informationabout that notable event, including an identification of the correlationsearch that generated the notable event.

The IT monitoring application provides pre-specified schemas forextracting relevant values from the different types of service-relatedevents. It also enables a user to define such schemas.

4.0. Data Fabric Service (DFS)

The capabilities of a data intake and query system are typically limitedto resources contained within that system. For example, the data intakeand query system has search and analytics capabilities that are limitedin scope to the indexers responsible for storing and searching a subsetof events contained in their corresponding internal data stores.

Even if a data intake and query system has access to external datastores that may include data relevant to a query, the data intake andquery system typically has limited capabilities to process thecombination of partial search results from the indexers and externaldata sources to produce comprehensive search results. In particular, thesearch head of a data intake and query system may retrieve partialsearch results from external data systems over a network. The searchhead may also retrieve partial results from its indexers, and combinethose partial search results with the partial results of the externaldata sources to produce final results for a query.

For example, the search head can implement map-reduce techniques, whereeach data source returns partial search results and the search head cancombine the partial search results to produce the final results of aquery. However, obtaining results in this manner from distributed datasystems including internal data stores and external data stores haslimited value because the search head can act as a bottleneck forprocessing complex search queries on distributed data systems. Thebottleneck effect at the search head worsens as the number ofdistributed data systems increases. Furthermore, even without processingqueries on distributed data systems, the search head 210 and theindexers 206 can act as bottlenecks due to the number of queriesreceived by the data intake and query system 108 and the amount ofprocessing done by the indexers during data ingestion, indexing, andsearch.

Embodiments of the disclosed data fabric service (DFS) system overcomethe aforementioned drawbacks by expanding on the capabilities of a dataintake and query system to enable application of a query acrossdistributed data systems, which may also be referred to as datasetsources, including internal data stores coupled to indexers (illustratedin FIG. 33 ), external data stores coupled to the data intake and querysystem over a network (illustrated in FIGS. 33, 46, 48 ), common storage(illustrated in FIGS. 46, 48 ), query acceleration data stores (e.g.,query acceleration data store 3308 illustrated in FIGS. 33, 46, 48 ),ingested data buffers (illustrated in FIG. 48 ) that include ingestedstreaming data. Moreover, the disclosed embodiments are scalable toaccommodate application of a query on a growing number of diverse datasystems.

In certain embodiments, the disclosed DFS system extends thecapabilities of the data intake and query system and mitigates thebottleneck effect at the search head by including one or more querycoordinators communicatively coupled to worker nodes distributed in abig data ecosystem. In some embodiments, the worker nodes can becommunicatively coupled to the various dataset sources (e.g., indexers,common storage, external data systems that contain external data stores,ingested data buffers, query acceleration data stores, etc.)

The data intake and query system can receive a query input by a user ata client device via a search head. The search head can coordinate with asearch process master and/or one or more query coordinators (the searchprocess master and query coordinators can collectively referred to as asearch process service) to execute a search scheme applied to one ormore dataset sources (e.g., indexers, common storage, ingested databuffer, query acceleration data store, external data stores, etc.). Theworker nodes can collect, process, and aggregate the partial resultsfrom the dataset sources, and transfer the aggregate results to a querycoordinator. In some embodiments, the query coordinator can operate onthe aggregate results, and send finalized results to the search head,which can render the results of the query on a display device.

Hence, the search head in conjunction with the search process master andquery coordinator(s) can apply a query to any one or more of thedistributed dataset sources. The worker nodes can act in accordance withthe instructions received by a query coordinator to obtain relevantdatasets from the different dataset sources, process the datasets,aggregate the partial results of processing the different datasets, andcommunicate the aggregated results to the query coordinator, orelsewhere. In other words, the search head of the data intake and querysystem can offload at least some query processing to the querycoordinator and worker nodes, to both obtain the datasets from thedataset sources and aggregate the results of processing the differentdatasets. This system is scalable to accommodate any number of workernodes communicatively coupled to any number and types of data sources.

Thus, embodiments of the DFS system can extend the capabilities of adata intake and query system by leveraging computing assets fromanywhere in a big data ecosystem to collectively execute queries ondiverse data systems regardless of whether data stores are internal ofthe data intake and query system and/or external data stores that arecommunicatively coupled to the data intake and query system over anetwork.

4.1. DFS System Architecture

FIG. 18 is a system diagram illustrating a DFS system architecture inwhich an embodiment may be implemented. The DFS system 200 includes adata intake and query system 202 communicatively coupled to a network ofdistributed components that collectively form a big data ecosystem. Thedata intake and query system 202 may include the components of dataintake and query systems discussed above including any combination offorwarders, indexers, data stores, and a search head. However, the dataintake and query system 202 is illustrated with fewer components to aidin understanding how the disclosed embodiments extend the capabilitiesof data intake and query systems to apply search queries and analyticsoperations on distributed data systems including internal data systems(e.g., indexers with associated data stores) and/or external datasystems in a big data ecosystem.

The data intake and query system 202 includes a search head 210communicatively coupled to multiple peer indexers 206 (also referred toindividually as indexer 206). Each indexer 206 is responsible forstoring and searching a subset of events contained in a correspondingdata store (not shown). The peer indexers 206 can analyze events for asearch query in parallel. For example, each indexer 206 can returnpartial results in response to a search query as applied by the searchhead 210.

The disclosed technique expands the capabilities of the data intake andquery system 202 to obtain and harmonize search results from externaldata sources 209, alone or in combination with the partial searchresults of the indexers 206. More specifically, the data intake andquery system 202 runs various processes to apply a search query to theindexers 206 as well as external data sources 209. For example, a daemon211 of the data intake and query system 202 can operate as a backgroundprocess that coordinates the application of a search query on theindexers and/or the external data stores. As shown, the daemon 211includes software components for the search head 210 and indexers 206 tointerface with a DFS master 212 and a distributed network of workernodes 214. In some embodiments, the worker nodes 214 may be consideredexternal to the data intake and query system 202. In certainembodiments, the worker nodes 214 may be considered part of the dataintake and query system 202.

The DFS master 212 is communicatively coupled to the search head 210 viathe daemon 211-3. In some embodiments, the DFS master 212 can includesoftware components running on a device of any system, including thedata intake and query system 202. As such, the DFS master 212 caninclude software and underlying logic for establishing a logicalconnection to the search head 210 when external data systems need to besearched. The DFS master 212 is part of the DFS search service (“searchservice”) that includes a search service provider 216 (also referred toas a query coordinator), which interfaces with the worker nodes 214.

Although shown as separate components, the DFS master 212 and the searchservice provider 216 are components of the search service that mayreside on the same machine, or may be distributed across multiplemachines. In some embodiments, running the DFS master 212 and the searchservice provider 216 on the same machine can increase performance of theDFS system by reducing communications over networks. As such, the searchhead 210 can interact with the search service residing on the samemachine or on different machines. For example, the search head 210 candispatch requests for search queries to the DFS master 212, which canspawn search service providers 216 of the search service for each searchquery.

Other functions of the search service provider 216 can include providingdata isolation across different searches based on role/access control,as well as fault tolerance (e.g., localized to a search head). Forexample, if a search operation fails, then its spawned search serviceprovider may fail but other search service providers for other searchescan continue to operate.

The search head 210 can analyze a query and determine that the DFSsystem 200 can execute the query. Accordingly, the search head 210 cansend the query to the query master 212, which can send it to, or spawn,a search service provider 216. The search service provider can define asearch scheme in response to a received search query that requiressearching both the indexers 206 and the external data sources 209. Aportion of the search scheme can be applied 210 to the indexers 206 andanother portion of the search scheme can be communicated to the workernodes 214 for application to the external data sources 209. The searchservice provider 216 can collect an aggregate of partial search resultsof the indexers 206 and of the external data sources 209 from the workernodes 214, and communicate the aggregate partial search results to thesearch head 210. In some embodiments, the DFS master 212, search head210, or the worker nodes 214 can produce the final search results, whichthe search head 210 can cause to be presented on a user interface of adisplay device.

More specifically, the worker nodes 214 can act as agents of the DFSmaster 212 via the search service provider 216, which can act on behalfof the search head 210 to apply a search query to distributed datasystems. For example, the DFS master 212 can manage different searchoperations and balance workloads in the DFS system 200 by keeping trackof resource utilization while the search service provider 216 isresponsible for executing search operations and obtaining the searchresults.

For example, the search service provider 216 can cause the worker nodes214 to apply a search query to the external data sources 209. The searchservice provider 216 can also cause the worker nodes 214 to collect thepartial search results from the indexers 206 and/or the external datasources 209 over the computer network. Moreover, the search serviceprovider 216 can cause the worker nodes 214 to aggregate the partialsearch results collected from the indexers 206 and/or the external datasources 209.

Hence, the search head 210 can offload at least some processing to theworker nodes 214 because the distributed worker nodes 214 can extractpartial search results from the external data sources 209, and collectthe partial search results of the indexers 206 and the external datasources 209. Moreover, the worker nodes 214 can aggregate the partialsearch results collected from the diverse data systems and transfer themto the search service, which can finalize the search results and sendthem to the search head 210. Aggregating the partial search results ofthe diverse data systems can include combining partial search results,arranging the partial search results in an ordered manner, and/orperforming operations derive other search results from the collectedpartial search results (e.g., transform the partial search results).

Once a logical connection is established between the search head 210,the DFS master 212, the search service provider 216, and the workernodes 214, control and data flows can traverse the components of the DFSsystem 200. For example, the control flow can include instructions fromthe DFS master 212 to the worker nodes 214 to carry out the operationsdetailed further below. Moreover, the data flow can include aggregatepartial search results transferred to the search service provider 216from the worker nodes 214. Further, the partial search results of theindexers 206 can be transferred by peer indexers to the worker nodes 214in accordance with a parallel export technique. A more detaileddescription of the control flow, data flow, and parallel exporttechniques are provided further below.

In some embodiments, the DFS system 200 can use a redistribute operatorof a data intake and query system. The redistribute operator candistribute data in a sharded manner to the different worker nodes 214.Use of the redistribute operator may be more efficient than the parallelexporting because it is closely coupled to the existing data intake andquery system. However, the parallel exporting techniques havecapabilities to interoperate with open source systems other than theworker nodes 214. Hence, use of the redistribute operator can providegreater efficiency but less interoperability and flexibility compared tousing parallel export techniques.

The worker nodes 214 can be communicatively coupled to each other, andto the external data sources 209. Each worker node 214 can include oneor more software components or modules 218 (“modules”) operable to carryout the functions of the DFS system 200 by communicating with the searchservice provider 216, the indexers 206, and the external data sources209. The modules 218 can run on a programming interface of the workernodes 214. An example of such an interface is APACHE SPARK, which is anopen source computing framework that can be used to execute the workernodes 214 with implicit parallelism and fault-tolerance.

In particular, SPARK includes an application programming interface (API)centered on a data structure called a resilient distributed dataset(RDD), which is a read-only multiset of data items distributed over acluster of machines (e.g., the devices running the worker nodes 214).The RDDs function as a working set for distributed programs that offer aform of distributed shared memory.

Thus, the search service provider 216 can act as a manager of the workernodes 214, including their distributed data storage systems, to extract,collect, and store partial search results via their modules 218 runningon a computing framework such as SPARK. However, the embodimentsdisclosed herein are not limited to an implementation that uses SPARK.Instead, any open source or proprietary computing framework running on acomputing device that facilitates iterative, interactive, and/orexploratory data analysis coordinated with other computing devices canbe employed to run the modules 218 for the DFS master 212 to applysearch queries to the distributed data systems.

Accordingly, the worker nodes 214 can harmonize the partial searchresults of a distributed network of data storage systems, and providethose aggregated partial search results to the search service provider216. In some embodiments, the search service provider 216 or DFS master212 can further operate on the aggregated partial search results toobtain final results that are communicated to the search head 210, whichcan output the search results as reports or visualizations on a displaydevice.

The DFS system 200 is scalable to accommodate any number of worker nodes214. As such, the DFS system can scale to accommodate any number ofdistributed data systems upon which a search query can be applied andthe search results can be returned to the search head and presented in aconcise or comprehensive way for an analyst to obtain insights into bigdata that is greater in scope and provides deeper insights compared toexisting systems.

4.2. DFS System Operations

FIG. 19 is an operation flow diagram illustrating an example of anoperation flow of the DFS system 200. The operation flow 2100 includescontrol flows and data flows of the data intake and query system 202,the DFS master 212 and/or the search service provider 216 (the DFSmaster 212 and search service provider 216 collectively the “searchservice 220”), one or more worker nodes 214, and/or one or more externaldata sources 209. A combination of the search service 220 and the workernodes 214 collectively enable the data fabric services that can beimplemented on the distributed data systems including, for example, thedata intake and query system 202 and the external data sources 209.

In step 2102, the search head 210 of the data intake and query system202 receives a search query. For example, an analyst may submit a searchquery to the search head 210 over a network from an application (e.g.,web browser) running on a client device, through a network portal (e.g.,website) administered by the data intake and query system 202. Inanother example, the search head 210 may receive the search query inaccordance with a schedule of search queries. The search query can beexpressed in a variety of languages such as a pipeline search language,a structured query language, etc.

In step 2104, the search head 210 processes the search query todetermine whether the DFS system 200 is to handle the search query. Insome embodiments, if the search query only requires searching theindexers 206, the search head 210 can conduct the search on the indexers206 by using, for example, map-reduce techniques without invoking orengaging the DFS system. In some embodiments, however, the search head210 can invoke or engage the DFS system to utilize the worker nodes 214to search the indexers 206 alone, search the external data sources 209alone, or search both and harmonize the partial search results of theindexers 206 alone, and return the search results to the search head 210via the search service 220.

If, search head 210 determines that the DFS system 200 is to handle thesearch query, then the search head 210 can invoke and engage the DFSsystem 200. Accordingly, in some embodiments, the search head 210 canengage the search service 220 when a search query is to be applied to atleast one external data system, such as a combination of the indexers206 and at least one of the external data sources 209, or is otherwiseto be handled by the DFS system 200. 210 The search head 210 can passsearch query to the DFS master 212, which can create (e.g., spawn) asearch service provider (e.g., search service provider 216) to conductthe search.

In some embodiments, the DFS system 200 can be launched by using amodular input, which refers to a platform add-on of the data intake andquery system 202 that can be accessed in a variety of ways such as, forexample, over the Internet on a network portal. For example, the searchhead 210 can use a modular input to launch the search service 220 andworker nodes 214 of the DFS system 200. In some embodiments, a modularinput can be used to launch a monitor function used to monitor nodes ofthe DFS system. In the event that a launched service or node fails, themonitor allows the search head to detect the failed service or node, andre-launch the failed service or node or launch or reuse another launchedservice or node to provide the functions of the failed service or node.In some embodiments, the monitor function for monitoring nodes can belaunched and controlled by the search service provider 216.

In step 2104, the search head 210 executes a search phase generationprocess to define a search scheme based on the scope of the searchquery. The search phase generation process involves an evaluation of thescope of the search query to define one or more phases to be executed bythe data intake and query system 202 and/or the DFS system, to obtainsearch results that would satisfy the search query. The search phases,or layers, may include a combination of phases for initiating searchoperations, searching the indexers 206, searching the external datasources 209, and/or finalizing search results for return back to thesearch head 210.

In some embodiments, the combination of search phases can include phasesfor operating on the partial search results retrieved from the indexers206 and/or the external data sources 209. For example, a search phasemay require correlating or combining partial search results of theindexers 206 and/or the external data sources 209. In some embodiments,a combination of phases may be ordered as a sequence that requires anearlier phase to be completed before a subsequent phase can begin.However, the disclosure is not limited to any combination or order ofsearch phases. Instead, a search scheme can include any number of searchphases arranged in any order that could be different from another searchscheme applied to the same or another arrangement or subset of datasystems.

For example, a first search phase may be executed by the search head 210to extract partial search results from the indexers 206. A second searchphase may be executed by the worker nodes 214 to extract and collectpartial search results from the external data sources 209. A thirdsearch phase may be executed by the indexers 206 and worker nodes 214 toexport partial search results in parallel to the worker nodes 214 fromthe (peer) indexers 206. As such, the third phase involves collectingthe partial search results from the indexers 206 by the worker nodes214. A fourth search phase may be executed by the worker nodes 214 toaggregate (e.g., combine and/or operate on) the partial search resultsof the indexers 206 and/or the worker nodes 214. A sixth and seventhphase may involve transmitting the aggregate partial search results tothe search service 220, and operating on the aggregate partial searchresults to produce final search results, respectively. The searchresults can then be transmitted to the search head 210. In some cases,an eighth search phase may involve further operating on the searchresults by the search head 210 to obtain final search results that canbe, for example, rendered on a user interface of a display device.

In step 2106, the search head 210 initiates a communications searchprotocol that establishes a logical connection with the worker nodes 214via the search service 220. Specifically, the search head 210 maycommunicate information to the search service 220 including a portion ofthe search scheme to be performed by the worker nodes 214. For example,a portion of the search scheme transmitted to the DFS master 212 mayinclude search phase(s) to be performed by the DFS master 212 and theworker nodes 214. The information may also include specific controlinformation enabling the worker nodes 214 to access the indexers 206 aswell as the external data sources 209 subject to the search query.

In step 2108, the search service 220 can define an executable searchprocess performed by the DFS system. For example, the DFS master 212 orthe search service provider 216 can define a search process as a logicaldirected acyclic graph (DAG) based on the search phases included in theportion of the search scheme received from the search head 210.

The DAG includes a finite number of vertices and edges, with each edgedirected from one vertex to another, such that there is no way to startat any vertex and follow a consistently-directed sequence of edges thateventually loops back to the same vertex. Here, the DAG can be adirected graph that defines a topological ordering of the search phasesperformed by the DFS system. As such, a sequence of the verticesrepresents a sequence of search phases such that every edge is directedfrom earlier to later in the sequence of search phases. For example, theDAG may be defined based on a search string for each phase or metadataassociated with a search string. The metadata may be indicative of anordering of the search phases such as, for example, whether results ofany search string depend on results of another search string such thatthe later search string must follow the former search stringsequentially in the DAG.

In step 2110, the search head 210 starts executing local search phasesthat operate on the indexers 206 if the search query requires doing so.If the scope of the search query requires searching at least oneexternal data system, then, in step 2112, the search head 210 sendsinformation to the DFS master 212 triggering execution of the executablesearch process defined in step 2108.

In step 2114, the search service 220 starts executing the search phasesthat cause the worker nodes 214 to extract partial search results fromthe external data stores 209 and collect the extracted partial searchresults at the worker nodes 214, respectively. For example, the searchservice 220 can start executing the search phases of the DAG that causethe worker nodes 214 to search the external data sources 209. Then, instep 2116, the worker nodes 214 collect the partial search resultsextracted from the external data sources 209.

The search phases executed by the DFS system can also cause the workernodes 214 to communicate with the indexers 206. For example, in step2118, the search head 210 can commence a search phase that triggers aremote pipeline executed on the indexers 206 to export their partialsearch results to the worker nodes 214. As such, the worker nodes 214can collect the partial search results of the indexers 206. However, ifthe search query does not require searching the indexers 206, then thesearch head 210 may bypass triggering the pipeline of partial searchresults from the indexers 206.

In step 2122, the worker nodes 214 can aggregate the partial searchresults and send them to the search service 220. For example, the searchservice provider 216 can begin collecting the aggregated search resultsfrom the worker nodes 214. The aggregation of the partial search resultsmay include combining the partial search results of indexers 206, theexternal data stores 209, or both. In some embodiments, the aggregatedpartial search results can be time-ordered or unordered depending on therequirements of the type of search query.

In some embodiments, aggregation of the partial search results mayinvolve performing one or more operations on a combination of partialsearch results. For example, the worker nodes 214 may operate on acombination of partial search results with an operator to output a valuederived from the combination of partial search results. Thistransformation may be required by the search query. For example, thesearch query may be an average or count of data events that includespecific keywords. In another example, the transformation may involvedetermining a correlation among data from different data sources thathave a common keyword. As such, transforming the search results mayinvolve creating new data derived from the partial search resultsobtained from the indexers 206 and/or external data sources 209.

In step 2124, a data pipeline is formed to the search head 210 throughthe search service 220 once the worker nodes 214 have received thepartial search results from the indexers 206 and the external datastores 209, and aggregated the partial search results (e.g., andtransformed the partial search results).

In step 2126, the aggregate search received by the search service 220may optionally be operated on to produce final search results. Forexample, the aggregate search results may include different statisticalvalues of partial search results collected from different worker nodes214. The search service 220 may operate on those statistical values toproduce search results that reflect statistical values of thestatistical values obtained from the all the worker nodes 214.

As such, the produced search results can be transferred in a big datapipeline to the search head 210. The big data pipeline is essentially apipeline of the data intake and query system 202 extended into the bigdata ecosystem. Hence, the search results are transmitting to the searchhead 210 where the search query was received by a user. Lastly, in step2128, the search head 210 can render the search results or dataindicative of the search results on a display device. For example, thesearch head 210 can make the search results available for visualizing ona user interface rendered via a computer portal.

It will be understood that fewer or more steps can be included in theoperation flow 2100. Further, some operations can be performed bydifferent components of the system. In some embodiments, for example,some of the tasks described as being performed by the search head 210can be performed by the search service 220, such as the search serviceprovider 216. As a non-limiting example, step 2104 can be omitted andsteps 2110, 2112, and 2118 can be performed by the search serviceprovider 216. For example, upon receiving the search query at step 2102,the search head 210 can determine that the DFS system 200 will handlethe query. Accordingly, at 2106, the search head can communicate thesearch query to the search service 220 to initiate the search. In turn,the search service provider 216 can define the search scheme 2104 andsearch process 2108. As part of defining the search scheme and process2108, the search service provider 216 can determine whether any indexers206 or external data sources 209 will be accessed. Once the scheme andprocess are defined, the search service provider 216 can trigger asearch of the indexers (2110) and an external search of the externaldata sources (2112). The partial search results from both can becommunicated to the worker nodes 214 for processing (2116, 2118), whichcan aggregate them together (2122). The results can then be provided tothe search service 220 (2124), further processed (2126), and thencommunicated to the search head 210 for rendering for the client device(2128). In some cases, the further processing 2126 performed by thesearch service 220 can include additional transforms on the resultsreceived from the worker nodes 214 based on the query. Accordingly, insuch an embodiment, the system can delegate some of the search head 210processing to the search service 220, thereby freeing up the search head210 to handle additional queries.

5.0. Parallel Export Techniques

The disclosed embodiments include techniques for exporting partialsearch results in parallel from peer indexers of a data intake and querysystem to the worker nodes. In particular, partial search results (e.g.,time-indexed events) obtained from peer indexers can be exported inparallel from the peer indexers to worker nodes. Exporting the partialsearch results from the peer indexers in parallel can improve the rateat which the partial search results are transferred to the worker nodesfor subsequent combination with partial search results of the externaldata systems. As such, the rate at which the search results of a searchquery can be obtained from the distributed data system can be improvedby implementing parallel export techniques.

FIG. 20 is an operation flow diagram illustrating an example of aparallel export operation performed in a DFS system according to someembodiments of the present disclosure. The operation 2200 for parallelexporting of partial search results from peer indexers 206 begins byprocessing a search query that requires transferring of partial searchresults from the peer indexers 206 to the worker nodes 214.

In step 2202, the search head 210 receives a search query as, forexample, input by a user of a client device. In step 2204, the searchhead 210 processes the search query to determine whether internal datastores 222 of peer indexers 206 must be searched for partial searchresults. If so, in step 2206, the search head 210 executes a process tosearch the peer indexers 206 and retrieve the partial search results. Instep 2209, each peer indexer 206 can return its partial search resultsretrieved from respective internal data stores 222.

In step 2210, the partial search results (e.g., time-indexed events)obtained by the peer indexers 206 can be sharded into chunks of events(“event chunks”). Sharding involves partitioning large data sets intosmaller, faster, more easily managed parts called data shards. Thesharded partitions can be determined from policies, which can be basedon hash values by default. Accordingly, the retrieved events can begrouped into chunks (e.g., micro-batches) based on a value associatedwith a search query and/or the corresponding retrieved events. Forexample, the retrieved events can be sharded in chunks based on thefield names passed as part of a search query process of the data intakeand query system. The event chunks can then be exported from the peerindexers 206 in parallel over the network to the worker nodes 214.

If time-ordering is required, the parallel exporting technique caninclude a mechanism to reconstruct the ordering of event chunks at theworker nodes 214. In particular, the order from which the event chunksflowed from peer indexers 206 can be tracked to enable collating thechunks in time order at the worker nodes 214. For example, metadata ofevent chunks can be preserved when parallel exporting such that thechunks can be collated by the worker nodes 214 that receive the eventchunks. Examples of the metadata include SearchResultsInfo (SRI) (a datastructure of SPLUNK® which carries control and meta information for thesearch operations) or timestamps indicative of, for example, the timeswhen respective events or event chunks started flowing out from the peerindexers 206. If time ordering is not required, preserving the timeordering of chunks by using timestamps may be unnecessary.

The parallel exporting technique can be modified in a variety of ways toimprove performance of the DFS system. For example, in step 2214, theevent chunks can be load balanced across the peer indexers 206 and/orreceiving worker nodes 214 to improve network efficiency and utilizationof network resources. In particular, a dynamic list of receivers (e.g.,worker nodes 214) can be maintained by software running on hardwareimplementing the DFS system. The list may indicate a currentavailability of worker nodes to receive chunks from export processors ofthe peer indexers 206. The list can be updated dynamically to reflectthe availability of the worker nodes 214. Further, parameters on thelist indicative of the availability of the worker nodes 214 can bepassed to the export processers periodically or upon the occurrence ofan event (e.g., a worker node 214 becomes available). The exportprocessers can then perform a load balancing operation on the eventchunks over the receiving worker nodes 214.

The worker nodes 214 may include driver programs that consume the eventsand event chunks. In some embodiments, the worker nodes 214 can includea software development kit (SDK) that allows third party developers tocontrol the consumption of events from the peer indexers 206 by theworker nodes 214. As such, third party developers can control thedrivers causing the consumption of events and event chunks from the peerindexers 206 by the worker nodes 214. Lastly, in step 2216, the eventchunks are exported from the peer indexers 206 in parallel to the workernodes 214.

In some embodiments, the rate of exporting events or event chunks inparallel by the peer indexers 206 can be based on an amount of sharedmemory available to the worker nodes 214. Accordingly, techniques can beemployed to reduce the amount of memory required to store transferredevents. For example, when the worker nodes 214 are not local (e.g.,remote from the peer indexers 206), compressed payloads of the eventchunks can be transferred to improve performance.

Thus, the disclosed DFS system can provide abig data pipeline and nativeprocessor as a mechanism to execute infrastructure, analytics, anddomain-based processors based on data from one or more external datasources over different compute engines. In addition, the mechanism canexecute parallelized queries to extract results from external systems.

It will be understood that fewer or more steps can be included in theoperation flow 2100. Further, some operations can be performed bydifferent components of the system. In some embodiments, for example,some of the tasks described as being performed by the search head 210can be performed by the search service 220, such as the search serviceprovider 216.

As a non-limiting example, the search head 210 can process the searchquery to determine whether the search query is to be handled by the DFSsystem 202. For example, in some embodiments, the search head 210 canhandle queries for the indexers 206 and in other embodiments, the searchservice 220 can handle queries for the indexers 206. Based on adetermination that the search process is to handle the search query, thesearch head 210 can forward the query to the search service 220. Thesearch service provider 216 can further process the query (2210) anddetermine that the search includes searching the indexer 206. As such,the search service provider can execute a process to search the peerindexers 206 and provide the partial search results to the worker nodes214, or instruct the worker nodes 214 to instruct the indexers 206 toexecute the search. Steps 2210, 2212, 2214, 2216, and 2218 can thenperform as illustrated such that the partial search results are exportedto the worker nodes 214 for further processing.

6.0. DFS Query Processing

The disclosed embodiments include techniques to process search queriesin different ways by the DFS system depending on the type of searchresults sought in response to a search query. In other words, a dataintake and query system can receive search queries that cause the DFSsystem to process the search queries differently based on the searchresults sought in accordance with the search queries. For example, somesearch queries may require ordered search results, and an order of thesearch results may be unimportant for other search queries.

To obtain ordered search results, a search query executed on internaldata sources (e.g., indexers) and/or external data sources may requiresorting and organizing timestamped partial search results across themultiple diverse data sources. However, the multiple internal orexternal data sources may not store timestamped data. That is, some datasources may store time-ordered data while other data sources may notstore time-ordered data, which prevents returning time-ordered searchresults for a search query. The disclosed embodiments provide techniquesfor harmonizing time-ordered and unordered data from across multipleinternal or external data sources to provide time-ordered searchresults.

In other instances, a search query may require search results thatinvolve performing a transformation of data collected from multipleinternal and/or external data sources. The transformed data can beprovided as the search results in response to the search query. In somecases, the search query may be agnostic to the ordering of the searchresults. For example, the search results of a search query may requirecounts of different types of events generated over the same period oftime. Hence, search results that satisfy the search query could beordered or unordered counts. As such, there is no requirement tomaintain the time order of the partial search results obtained from datasystems subject to the count search query. Thus, the techniquesdescribed below provide mechanisms to obtain search result from the bigdata ecosystem that are transformed, time-ordered, unordered, or anycombinations of these types of search results.

6.1. Ordered Search Results

The disclosed embodiments include techniques to obtain ordered searchresults based on partial search results from across multiple diverseinternal and/or external data sources. The ordering of the searchresults may be with respect to a parameter associated with the partialsearch results. An example of a parameter includes time. As such, thedisclosed technique can provide a time-ordered search result based onpartial search results obtained from across multiple internal and/orexternal data sources. Moreover, the disclosed technique can providetime-ordered search results regardless of whether the partial searchresults obtained from the diverse data sources are timestamped.

An ordered search (e.g., ordered data execution) can be referred to as“cursored” mode of data access. According to this mode of data access,the DFS system can execute time-ordered searches or retrieve events frommultiple data sources and presents the events in a time ordered manner.For searches involving only local data sources, the DFS system canimplement a micro-batching mechanism based on the event time acrossworker nodes. The DFS system can ensure that per peer ordering isenforced across the worker nodes and final collation is performed at alocal search head or search service provider. In case of event retrievalfrom multiple data sources, the DFS system can maintain per sourceordering prior to ordered collation in the local search head or searchservice provider.

FIG. 21 is a flowchart illustrating a method 2300 performed the DFSsystem to obtain time-ordered search results in response to a cursoredsearch query according to some embodiments of the present disclosure. Asdescribed below, the method 2300 for processing cursored search queriescan involve a micro-batching process executed by worker nodes to ensuretime orderliness of partial search results obtained from data sources.

In step 2302, one or more worker nodes collect partial search resultsfrom the internal and/or external data sources. For example, the workernode may collect partial search results corresponding to data having adata structure as specified by the search query. In another example, theworker nodes may query an external data source for partial searchresults based on specific keywords specified by a cursored search query,and collect the partial search results. The worker nodes may alsocollect partial search results from indexers, which were returned inresponse to application of the search query by the search head (orsearch service provider) to the indexers. In some embodiments, thepartial search results may be communicated from each data source to theworker node in chunks (e.g., micro-batches).

In step 2304, the worker nodes perform deserialization of the partialsearch results obtained from the data sources. Specifically, partialsearch results transmitted by the data sources could been serializedsuch that data objects were converted into a stream of bytes in order totransmit the object, or store the object in memory. The serializationprocess allows for saving the state of an object in order to reconstructit at the worker node by using reverse process of deserialization.

In step 2306, the worker nodes receive the partial search resultscollected from the data sources and transform them into a specifiedformat. As such, partial search results in diverse formats can betransformed into a common specified format. The specified format may bespecified to facilitate processing by the worker nodes. Hence, diversedata types obtained from diverse data sources can be transformed into acommon format to facilitate subsequent aggregation across all thepartial search results obtained in response to the search query. As aresult, the partial search results obtained by the worker nodes can betransformed into, for example, data events having structures that arecompatible to the data intake and query system.

In step 2308, the worker nodes may determine whether the partial searchresults are associated with respective time values. For example, theworker nodes may determine that events or event chunks from an internaldata source are timestamped as shown in FIG. 2 , but events or eventchunks from an external data source may not be timestamped. Thetimestamped events may also be marked with an “OriginType” (e.g.,mysql-origin, cloud-aws-s), “SourceType” (e.g., cvs, json, sql), and“Host< >” (e.g., IP address where the event originated), or other datauseful for ordering the partial search. If all the partial searchresults from across the diverse data systems are adequately marked, thenharmonizing the partial search results may not require different typesof processing. However, typically at least some partial search resultsfrom across the diverse distributed data systems are not adequatelymarked to facilitate harmonization.

Accordingly, the worker nodes can implement bifurcate processing of thepartial search results depending on whether or not the partial searchresults are adequately marked. Specifically, the partial search resultsthat are timestamped can be processed one way, and the partial searchresults that are not timestamped can be processed a different way. Theworker nodes can execute the different types of processinginterchangeably, or execute one type of processing after the other typeof processing has completed.

In step 2310, for time-ordered partial search results, respective workernodes can be assigned (e.g., fixed) to receive time-ordered partialsearch results (e.g., events or event chunks) from respective datasources in an effort to maintain the time orderliness of the data.Assigning a worker node to obtain time-ordered partial search results ofthe same data source avoids the need for additional processing amongmultiple nodes otherwise required if they each received differenttime-ordered chunks from the same data source. In other words, setting aworker node to collect all the time-ordered partial search results fromits source avoids the added need to distribute the time-ordered partialsearch results between worker nodes to reconstruct the overall timeorderliness of the partial search results.

For example, a worker node can respond to timestamped partial searchresults it receives by setting itself (or another worker node) toreceive all of the partial search results from the source of thetime-stamped partial search results. For example, the worker node can beset by broadcasting the assignment to other worker nodes, whichcollectively maintain a list of assigned worker nodes and data sources.In some embodiments, a worker node that receives timestamped partialsearch results can communicate an indication about the timestampedpartial search results to the DFS master or search service provider.Then the DFS master or search service provider can set a specific set ofworker nodes to receive all the timestamped data from the specificsource.

In step 2312, the worker nodes read the collected partial search results(e.g., events or event chunks) and arrange the partial search results intime order. For example, each collected event or event chunk may beassociated with any combination of a start time, an end time, a creationtime, or some other time value. The worker node can use the time values(e.g., timestamps) associated with the events or event chunks to arrangethe events and/or the event chunks in a time-order. Lastly, in step2314, the worker nodes may stream the time-ordered partial searchresults in parallel as time-ordered chunks via the search service (e.g.,to the DFS master or search service provider of the DFS system).

Referring back to step 2308, the worker nodes can respond differently topartials search results that are not associated with timestamps (e.g.,lack an associated time value that facilitates time ordering). In step2316, the worker nodes can associate events or chunks with a time valueindicative of the time of ingestion of the events or event chunks by therespective worker nodes (e.g., an ingestion timestamp). The worker nodescan associate the partial search results with any time value that can bemeasured relative to a reference time value (e.g., not limited to aningestion timestamp). In some embodiments, the partial search resultstimestamped by the worker nodes can also be marked with a flag todistinguish those partial search results from the partial search resultsthat were timestamped before being collected by the worker nodes.

In step 2318, the worker nodes sort the newly timestamped partial searchresults and create chunks (e.g., micro-batches) upon completion ofcollecting all of the partial search results from the data sources. Insome embodiments, the chunks may be created to contain a default minimumor maximum number of partial search results (e.g., a default chunksize). As such, the worker nodes can create time-ordered partial searchresults obtained from data sources that did not provide time-orderedpartial search results.

In step 2320, the worker nodes can apply spillover techniques to disk asneeded. In some embodiments, the worker nodes can provide an extensiveHB/status update mechanism to notify the DFS master of its currentblocked state. In some embodiments, the worker nodes can ensure akeep-alive to override timeout and provide notifications. Lastly, instep 2322, the worker nodes may stream the time-ordered partial searchresults in parallel as time-ordered chunks via the search service (e.g.,to the DFS master or search service provider of the DFS system).

Accordingly, time-ordered partial search results can be created from acombination of time-ordered and non-time-ordered partial searchcollected from diverse data sources. The time-ordered partial searchresults can be streamed in parallel from multiple worker nodes to theservice provider, which can stream each search stream to the search headof the data intake and query system. As such, time-ordered searchresults can be produced from diverse data types of diverse data systemswhen the scope of a search query requires doing so.

FIG. 22 is a flowchart illustrating a method 2400 performed by a dataintake and query system of a DFS system in response to a cursored searchquery according to some embodiments of the present disclosure.Specifically, the method 2400 can be performed by the data intake andquery system to collate the time-ordered partial search results obtainedby querying internal and/or external data sources.

In step 2402, the search head, search service provider, or one or moreworker nodes receive one or more streams of time-ordered partial searchresults (e.g., event chunks) from a data source. In step 2404, thesearch head or search service provider creates multiple searchcollectors to collect the time ordered event chunks.

For example, the search head or search service provider can add a classof collectors to collate search results from the worker nodes. In someembodiments, the search head or search service provider can createmultiple collectors; such as a collector for each indexer, as well as asingle collector for each external data source or other data source. Insome embodiments, the search head or search service provider may createa collector for each stream, which could include time-ordered chunksfrom a single worker node or a single data source. Hence, each collectorreceives time-ordered chunks.

In step 2406, the collectors perform a deserialization process on thereceived chunks and their contents, which had been serialized fortransmission from the search service. In step 2408, each collector addsthe de-serialized partial search or their chunks to a collector queue.The search head or search service provider may include any number ofcollector queues. For example, the search head or search serviceprovider may include a collector queue for each collector or for eachdata source that provided partial search results.

In step 2410, the search head, search service provider, or designatedworker node(s) can collate the time-ordered partial search resultsobtained from the data sources as time-ordered search results of thepresented search query. For example, the search head, search serviceprovider, or designated worker node(s) may apply a collation operationbased on the time-order of events contained in the chunks from thequeues of different collectors to provide time-ordered search results.

Lastly, in step 2412, the time-ordered search results could be providedto an analyst on a variety of mediums and in a variety of formats. Forexample, the time-ordered search results may be rendered as a timelinevisualization on a user interface on a display device. In someembodiments, the raw search results (e.g., entire raw events) areprovided for the timeline visualization.

The visualization can allow the analyst to investigate the searchresults. In another example, the time-ordered results may be provided toan analyst automatically on printed reports, or transmitted in a messagesent over a network to a device to alert the analyst of a conditionbased on the search results.

Although the methods illustrated in FIGS. 21 and 22 include acombination of steps to obtain time-ordered search results from acrossdiverse data sources that may or may not provide timestamped data, thedisclosed embodiments are not so limited. Instead, any portion of thecombination of steps illustrated in FIGS. 21 and 22 could be performeddepending on the scope of the search query. For example, only a subsetof steps may be performed when the search results for a search query areobtained exclusively from a single external data source that storestimestamped data.

6.2. Transformed Search Results

The disclosed embodiments include a technique to obtain search resultsfrom the application of transformation operations on partial searchresults obtained from across internal and/or external data sources.Examples of transformation operations include arithmetic operations suchas an average, mean, count, or the like. Examples of reportingtransformations include join operations, statistics, sort, top head.Hence, the search results of a search query can be derived from partialsearch results rather than include the actual partial search results. Inthis case, the ordering of the search results may be nonessential. Anexample of a search query that requires a transformation operation is a“batch” or “reporting” search query. The related disclosed techniquesinvolve obtaining data stored in the big data ecosystem, and returningthat data or data derived from that data.

According to a reporting or batch mode of data access, the DFS systemexecutes blocking transforming searches, for example, to join across oneor multiple available data sources. Since ordering is not needed, theDFS system can implement sharding of the data from the various datasources and execute aggregation (e.g., reduction of map-reduction) inparallel. The DFS architecture can also execute multiple DFS operationsin parallel to receive sharded data from the different sources.

FIG. 23 is a flowchart illustrating a method 2500 performed by nodes ofa DFS system to obtain search results in response to a batch orreporting search query according to some embodiments of the presentdisclosure. The method 2500 for processing batch or reporting searchqueries can involve steps performed by the DFS master, the serviceprovider, and/or worker nodes to transform partial search results intosearch results into batch or reporting search results. The disclosedtechniques also support both streaming and non-streaming for multipledata sources.

The transformation operations generally occur at the worker nodes. Forexample, an operation may include a statistical count of events having aparticular IP address. The DFS can shard the data in certain partitions,and then each worker node can apply the transformation to thatparticular partition. In case it is the last reporting/transformingprocessor, then the transformed results are collated at the searchservice provider, and then transmitted to the search head. However, ifthere is a reporting search beyond the statistical count, then anotherreshuffle of the partial search results can be executed among the workernodes to put the different partitions on the same worker node, and thentransforms can be applied. If this is the last reporting search, thenresults are sent back to the service provide node and then to the searchhead. This process continues as dictated by the DAG generated from thephase desired by the search head.

In step 2502, the worker nodes collect partial search results from theinternal and/or external data sources. For example, a worker node maycollect partial search results including data having data structuresspecified by the search query. In another example, the worker node mayquery an external data source for partial search results based onspecific keywords included in a reporting search query, and collect thepartial search results. The worker node may also collect partial searchresults from indexers, which were returned in response to application ofthe reporting search query by the search head (or search serviceprovider or nodes) to the indexers. The partial search results may becommunicated from each data source to the worker nodes individually orin chunks (e.g., micro-batches). The worker nodes thus ingest partialsearch results obtained from the data sources in response to a searchquery.

In step 2504, the worker nodes can perform deserialization of thepartial search results obtained from the data sources. Specifically, thepartial search results transmitted by the data sources can be serializedby converting objects into a stream of bytes, which allows for savingthe state of an object for subsequent recreation of the object at theworker nodes by using the reverse process of deserialization.

In step 2506, the worker nodes transform the de-serialized partialsearch results into a specified format. As such, partial search resultscollected in diverse formats can be transformed into a common specifiedformat. The specified format may be specified to facilitate processingby a worker node. As such, diverse data types obtained from diverse datasources can be transformed into a common format to facilitate subsequentaggregation across all the partial search results obtained in responseto the search query. As a result, the partial search results obtained byworker nodes can be transformed into, for example, data events havingstructures that are compatible to the data intake and query system.

Unlike cursored search queries, the time-order of partial search resultsis not necessarily considered when processing reporting queries.However, in step 2508, if a data source returns partial search resultsthat are not associated with time values (e.g., no timestamp), theworker nodes can associate events or event chunks with a time valueindicative of the time of ingestion of the events or chunks by theworker nodes (e.g., ingestion timestamp). In some embodiments, theworker nodes can associate the partial search results with any timevalue that can be measured relative to a reference time value.Associating time values with partial search results may facilitatetracking partial search results when processing reporting searches, ormay be necessary when performing reporting searches that requiretime-ordered results (e.g., a hybrid of cursored and reportingsearches).

In step 2510, the worker nodes determine whether the ingested partialsearch results were obtained by an internal data source or an externaldata source to bifurcate processing respectively. In other words, theworker nodes process the ingested partial search results differentlydepending on whether they were obtained from an internal data source(e.g., indexers) or an external data source, if needed. That is, thiscan be the case only when reporting searches are run in the indexers;however, if all the processors in the indexers are streaming, then noprocessing unique to the indexer data is needed. However, data fromexternal data sources can be sanitized in terms of coding, timestamped,and throttles based on the timestamp.

In step 2512, for internal data sources, the worker nodes read thepartial search results obtained from indexers of a data intake and querysystem in a sharded way. In particular, the worker nodes may use a listidentifying indexers from which to pull the sharded partial searchresults. As discussed above, sharding involves partitioning datasetsinto smaller, faster, and more manageable parts called data shards. Thesharded partitions can be determined from policies, which can be basedon hash values by default. In the context of map-reduce techniques, themap step can be determined by the sharding and a predicate passed, whichmaps records matching the predicate to whatever is needed as the searchresult. The reduce step involves the aggregation of the shards. Theresults of a query are those items for which the predicate returns true.

In step 2514, the partial search results of the indexers are aggregated(e.g., combined and/or transformed) by the worker nodes. In particular,the partial search results can be in a pre-streaming format(semi-reduced), and need to be aggregated (e.g., reduced or combined)prior to aggregation with partial search results of external datasources. In step 2516, the aggregated partial search results of theindexers are aggregated (e.g., combined and/or transformed) with thepartial search results obtained from external data sources. Lastly, instep 2518, the aggregated partial search results of internal andexternal data stores can be transmitted from the worker nodes inparallel to the search service (e.g., to the DFS master or searchservice provider of the DFS system).

In step 2520, for external data sources, the worker nodes pushpredicates for the reporting search query to the external data sources.A predicate is a function that takes an argument, and returns a Booleanvalue indicating of true or false. The predicate can be passed as aquery expression including candidate items, which can be evaluated toreturn a true or false value for each candidate item.

In step 2522, the network nodes can determine whether the external datasources may or may not be able to execute a sharded query. In step 2526,for an external data source that can execute a sharded query, the workernode reads the results in different shards. In some embodiments, the DFSmaster randomly chooses which worker nodes will execute the shards. Instep 2524, for an external data sources that cannot execute a shardedquery, a worker node has the ability to spillover to disk, andredistribute to other worker nodes.

In step 2528, the worker nodes can apply an aggregation (e.g., (e.g.,combine and/or transform) or stream processing to have the partialsearch results ready for further processing against results from partialsearch results from the internal sources. Thus, referring back to step2516, the worker nodes aggregate the partial search results from alldata sources in response in response to the search query. For example,the worker nodes can apply a process similar to a reduction step of amap-reduce operation across all the partial search results obtained fromdiverse data sources. Then, in step 2518, the aggregate partial searchresults can be transmitted from the worker nodes in parallel to thesearch service provider 216. In particular, the search service provider,can collect all the finalized searches results from the worker nodes,and return the results to the search head.

FIG. 24 is a flowchart illustrating a method performed by a data intakeand query system of a DFS system in response to a batch or reportingsearch query according to some embodiments of the present disclosure. Inparticular, the method 2600 is performed by the data intake and querysystem to provide the batch or reporting search results obtained byquerying internal and/or external data sources.

In step 2602, a search head, search service provider, or designatedworker node(s) of receives the aggregate partial search results via ahybrid collector. The number and function of the hybrid collectors isdefined depending on the type of search executed. For example, for thetransforming search, the search head or search service provider cancreate only one collector to receive the final results from the workernodes and after serialization directly pushes into the search resultqueue. In step 2604, the search head or search service provider uses anexisting job pool to de-serialize search results, and can push thesearch results out. In such an operation, collation is not needed.

Lastly, in step 2606, the transformed search results could be providedto an analyst on a variety of mediums and in a variety of formats. Forexample, the time-ordered search results may be rendered as a timelinevisualization on a user interface on a display device. The visualizationcan allow the analyst to investigate the search results. In anotherexample, the time-ordered results may be provided to an analystautomatically on printed reports, or transmitted in a message sent overa network to a device to alert the analyst of a condition based on thesearch results.

Although the methods illustrated in FIGS. 23 through 26 include acombination of steps to obtain time ordered, unordered, or transformedsearch results from across multiple data sources that may or may notstore timestamped data, the disclosed embodiments are not so limited.Instead, a portion of a combination of steps illustrated in any of thesefigures could be performed depending on the scope of the search query.For example, only a subset of steps may be performed when the partialsearch results for a search query is obtained exclusively from anexternal data source.

7.0. Co-Located Deployment Architecture

The capabilities of a data intake and query system can be improved byimplementing the DFS system described above in a co-located deploymentwith the data intake and query system. For example, FIG. 25 is a systemdiagram illustrating a co-located deployment of a DFS system with thedata intake and query system in which an embodiment may be implemented.

In the illustrated embodiment, the system 224 shows only some componentsof a data intake and query system but can include other components(e.g., forwarders, internal data stores) that have been omitted forbrevity. In particular, the system 224 includes search heads 226-1 and226-2 (referred to collectively as search heads 226). The search heads226 collectively form a search head cluster 228. Although shown withonly two search heads, the cluster 228 can include any number of searchheads. Alternatively, an embodiment of the co-located deployment caninclude a single search head rather than the cluster 228.

The search heads 226 can operate alone or collectively to carry outsearch operations in the context of the co-located deployment. Forexample, a search head of the cluster 228 can operate as a leader thatorchestrates search. As shown, the search head 226-1 is a leader of thecluster 228. Any of the search heads 226 can receive search queries thatare processed collectively by the cluster 228. In some embodiments, aparticular search head can be designated to receive a search query andcoordinate the operations of some or all of the search heads of acluster 228. In some embodiments, a search head of the cluster 228 cansupport failover operations in the event that another search head of thecluster 228 fails.

In various implementations, one of the search heads of the cluster 228may operate as a “captain” among the search heads of the cluster 228.The search head captain (which may also be referred to as a search headcluster captain) may be the same as the leader described in thepreceding paragraph, or it may perform a different role. The search headcaptain may be a coordinating component that serves to orchestratesearch and coordinate communication between members of the searchenvironment, such as communication between the search heads of thecluster 228. In some implementations, any of the search heads of thecluster 228 may be able to interchangeably act as the search headcaptain as the need arises, such that there is no single search headthat is permanently designated as the captain. Thus, if a search headcaptain fails or goes down, another search head in the cluster 228 maybe selected to become the next search head captain as part of a recoveryprotocol.

Any suitable method of selecting a search head to serve as the searchhead captain may be used. Some approaches for selecting the search headcaptain are described in U.S. Pub. No. 2016/0034490, titled “INTELLIGENTCAPTAIN SELECTION FOR DISASTER RECOVERY OF SEARCH HEAD CLUSTER”, whichis hereby incorporated by reference for all purposes as if fully setforth herein. As one example, a search head in the cluster 228 may beelected to be the search head captain through an election protocol suchas a Raft consensus algorithm (e.g., based on voting performed by thesearch heads in the cluster 228), with the remainder of the search headsin the cluster 228 being followers. In some implementations, thefollower search heads in the cluster 228 may engage in intra-clustercommunications exclusively with the search head captain (i.e., there isno follower-to-follower search head communication).

In various implementations, the search head captain may be configured toupdate, generate/replicate, and distribute configuration files acrossthe search heads 226 of the cluster 228 to ensure that all the searchheads are synchronously operating with the same configuration. In somecases, the search head captain may be capable of leveraging the existinginfrastructure or protocols of the data intake and query system in orderto distribute configuration files to other search heads.

Any suitable method of replicating a configuration across the searchhead cluster 228 may be used. Some approaches for configurationreplication are described in U.S. Pub. No. 2018/0314601, titled“CONFIGURATION REPLICATION IN A SEARCH HEAD CLUSTER”, which is herebyincorporated by reference for all purposes as if fully set forth herein.As one example, the search head captain may be responsible forsynchronizing knowledge object customizations (e.g., any action relatingto a knowledge object, such as, for example, the deletion, creation,modification, change, or update of a knowledge object) across searchheads in the cluster 228, including configurations associated withsearch and/or visualization of the data intake and query system(exemplary knowledge objects may include but are not limited to, a savedsearch, an event type, a transaction, a tag, a field extraction, a fieldtransform, a lookup, a workflow action, a search command, a view, andlate-binding schema).

The cluster 228 is coupled to N peer indexers 230. In particular, thesearch head 226-1 can be a leader or search head captain of the cluster228 that is coupled to each of the N peer indexers 230. The system 224can run one or more daemons 232 that can carry out the DFS operations ofthe co-located deployment. In particular, the daemon 232-1 of the searchhead 226-1 is communicatively coupled to a DFS master 234, whichcoordinates control of DFS operations. Moreover, each of the N peerindexers 230 run daemons 232 communicatively coupled to respectiveworker nodes 236. The worker nodes 236 are coupled to one or more datasources from which data can be collected as the partial search resultsof a search query. For example, the worker nodes 236 can collect partialsearch results of the indexers from internal data sources (not shown)and one or more of external data sources 240. Lastly, the worker nodes236 are communicatively coupled to the DFS master 234 or a searchservice provider to form the DFS architecture of the illustratedco-located embodiment.

More specifically, the worker nodes 236 may be communicatively coupledto the DFS master 234 as part of a distributed computing framework(e.g., SPARK) used to execute the worker nodes 236 with implicitparallelism and fault-tolerance. The DFS master 234 may be a softwarecomponent or instance of the distributed computing framework that islaunched on a search head, the worker nodes 236 may be softwarecomponents or instances of the distributed computing framework that arelaunched on the indexers 230, and the worker nodes 236 may act as agentsof the DFS master 234 (e.g., the DFS master 234 may provide instructionsto the worker nodes 236 to carry out a search operation using theresources of the indexers 230).

In various implementations, the software for the DFS master 234 (e.g.,the application or program instructions executed to run the DFS master234) may be installed on each of the search heads 226 of the cluster228. Furthermore, each of the search heads 226 may also have anadditional software or process that is installed and running on it(e.g., daemon 232), which can launch and activate the DFS master 234(e.g., by triggering execution of the installed DFS master software).Thus, the DFS master 234 may be launched on any of the search heads 226of the cluster 228.

However, in practice, only one of the search heads 226 should launch theDFS master 234 to ensure that there is only one instance of the DFSmaster 234 running at a time. In some implementations, this singleinstance of the DFS master 234 may also start on the search head captain(e.g., search head 226-1, as depicted in FIG. 25 ). Once launched by thesearch head captain, the DFS master 234 may run alongside the searchhead captain in order to address the issue of high availability. In somecases, this may be implemented by taking advantage of existing protocolsin the data intake and query system for identifying a search headcaptain among the search heads of the cluster 228 in any circumstance,such that the role is reserved. Accordingly, the DFS master 234 may beconfigured to run specifically on the search head captain bypiggybacking on the search head captain selection mechanism (e.g., theDFS master 234 may be launched by a search head upon its election as thesearch head captain). If a search head captain fails or goes down,another search head in the cluster 228 may be selected to become thenext search head captain (e.g., in accordance with a recovery protocol)and it may also launch the DFS master 234 using the software (e.g., DFSmaster software) installed on it. In this manner, the DFS master 234 mayalways be run on the current search head captain.

In various implementations, the software for the worker nodes 236 (e.g.,the application or program instructions executed to run the worker nodes236) may be installed on each of the peer indexers 230. Furthermore,each of the peer indexers 230 may also have an additional software orprocess that is installed and running on it (e.g., daemon 232), whichcan launch and activate the worker node (e.g., by triggering executionof the installed worker node software). Thus, each of the peer indexers230 may be able to launch an instance of a worker node 236 using thesoftware (e.g., worker node software) installed on it.

In various implementations, installation of both the software for theDFS master 234 (on the search heads 226 of the cluster 228) and thesoftware for the worker nodes 236 (on the indexers 230) may occur at aninitialization step or any time that a new search head or indexer iscreated and added to the system. A deployer for the search heads 226 maydeploy the software for the DFS master 234 by installing it on each ofthe search heads 226, and a deployer for the indexers 230 may deploy thesoftware for the worker nodes 236 by installing it on each of theindexers 230. The role of these deployers may be to make installation ofthe software on each of the search heads 226 and indexers 230 easier,since the deployers can simply push the software to those components andrestart them after installation has been completed. Thus, the softwarefor the DFS master 234 and the worker nodes 236 can be deployed to, andinstalled on, the search heads 226 and indexers 230, respectively.Afterwards, those search heads 226 and indexers 230 may be restarted.

In various implementations, the search heads 226 and indexers 230 mayhave an additional software or process or agent that is installed andrunning on them (e.g., daemon 232), which may run continuously in thebackground of the search heads 226 and indexers 230 after installationof the corresponding software for the DFS master 234 or the worker nodes236. For instance, a daemon 232 may run in the background of each of thesearch heads 226 and indexers 230 to perform monitoring and variousother functions.

In various implementations, the daemon 232 running on each of the searchheads 226 may periodically check the respective search head that it isinstalled on to determine if that search head is now the search headcaptain (e.g., in the event that a search head is elected to be searchhead captain, such as if a previous search head captain failed). If thesearch head is not the search head captain, then the daemon 232 may donothing but continue to periodically check if the search head is thesearch head captain. However, for one of the search heads, the daemon232 may determine that the search head is elected to role of search headcaptain (e.g., if there were no prior search head captains or a previoussearch head captain failed). The daemon 232 may then launch and activatethe DFS master 234 by running the software for the DFS master 234 thatwas installed on the search head (now the search head captain). Byhaving the daemon 232 perform this monitoring, it ensures that the DFSmaster 234 will be run on the current search head captain.

It should be noted that after the DFS master 234 has been launched onthe search head captain (e.g., search head 226-1), the search headcaptain, the DFS master 234 running on the search head captain, and/orthe daemon 232 running on the search head captain may interchangeablyfulfill various responsibilities and perform tasks described herein.However, for the purpose of facilitating ease of understanding, thesetasks will be described as being performed by the search head captain,but it should be understood that any suitable component, software, orprocess of the search head captain may be initiating the performance ofthese tasks.

With respect to these tasks, the search head captain may generate a newshared secret key that will be used by the entire system 224 (e.g., forprocessing a search using the distributed computing framework). Inparticular, an indexer 230 may only be permitted to launch and run aworker node 236 as part of the distributed computing framework if theindexer 230 is in possession of the shared secret key.

The search head captain may also update a DFS configuration fileresiding on the search head captain. In particular, the search headcaptain may update the DFS configuration file with information such asan instance identifier of the DFS master 234 (e.g., a DFS masterinstance identifier), the shared secret key selected by the DFS master234 (e.g., a DFS master shared secret key), the IP address and portassociated with the DFS master 234 (e.g., IP address and port of thesearch head captain), and a list of active indexers. The list of activeindexers may consist of indexers (or identifiers of the indexers) thathave been selected for launching and running worker nodes (e.g., aselected subset of all the indexers 230 capable of launching and runningworker nodes because a particular user may not wish to use all theavailable indexers 230, but instead may wish to use certain indexersbased on criteria like the load on the indexers or the configuration ofthose indexers). The search head captain may be configured to retrieve alist of active indexers.

In some implementations, this list of active indexers may initially bean empty set. However, in some implementations, a user may be able toadd to this list of active indexers in various ways. For instance, theremay be a web-based user interface (e.g., accessible through a web URL)associated with the DFS master 234 running on the search head captain.The web-based UI may provide a user with a list of all the indexers 230in the system 224 that are capable of launching and running worker nodes236 (e.g., that have the software for the worker nodes 236 installed).Through this web-based UI, a user may be able to select any of theindexers 230 and add them to the list of active indexers (e.g., theindexers qualified to launch and run worker nodes). Upon doing so, theweb-based UI may trigger the update of the list of active indexers inthe DFS configuration file (e.g., the backend API of the web-based UImay trigger a call to the daemon 232, which may add the updated list ofactive indexers to the configuration file). So for example, if the userselected indexers {1, 2, and 3} to be added to the list of activeindexers, there would then be identifiers for the indexers {1, 2, and 3}in the updated list of active indexers. In some implementations, thelist of active indexers may contain identifiers associated with theindexers, such as IP address, hostname, or some other global unique ID(GUID) for the indexers. In some cases, using GUIDs in the list ofactive indexers may be beneficial, especially when the IP address orhostname associated with the indexers may be affected by the presence ofa firewall or network address translation (NAT).

In some implementations, there may be a workload management feature thatmay be implemented by notifying a user of indexer capacity orrestricting the indexers that the user is able to pick and choose fromto add to the list of active indexers. For instance, the web-based UImay warn or notify a user about the capacity of an indexer if the useris attempting to add an indexer that is above or below a certaincapacity threshold (e.g., warn if the index has less than 20% availablecapacity) to the list of active indexers. Or the indexers may not beavailable to add to the list of active indexers unless they havecapacity or processing availability over or under a certain configurablethreshold (e.g., only display indexers that have >50% capacity to theuser via the web-based UI). In some implementations, this workloadmanagement feature may be implemented elsewhere, such as by having anindexer 230 perform an additional check on its own capacity once itdetermines that it is in the list of active indexers and launching aworker node only if that capacity is over the threshold. Oralternatively, worker nodes can be instantiated by all the activeindexers, but only the worker nodes that correspond to indexers withsufficient capacity may be used at the time of processing a DFS search.

In various implementations, the search head captain may be configured topropagate to the other search heads 226 in the cluster 228 any updatesmade to this DFS configuration file. After updating the DFSconfiguration file, the search head captain may initiate communicationswith each of the other search heads 226 in the cluster 228, for whichthe search head captain may replicate its updated DFS configuration fileand distribute a copy of it to each of the search heads 226 in thecluster 228. Thus, these communications may inform the other searchheads 226 that the search head captain is the current captain runningthe DFS master 234, which can be connected to using the IP address andport provided in the DFS configuration file. In some implementations,the search head captain may implement replication and distribution ofthe DFS configuration file to all the search heads 226 using an existingmechanism in the system, such as a horizontal replication mechanism.When the search heads 226 receive the DFS configuration file from thesearch head captain, each of the search heads 226 may save the DFSconfiguration file locally to be applied to a future DFS search. Thus,any updates to the DFS configuration file at the search head captain aresynchronized and propagated to all the other search heads 226, so evenif the search head captain dies and another search head is elected totake over and become the new search head captain, the new search headcaptain would start off with the latest configuration file information.It should also be noted that once indexers are selected and added to thelist of active indexers in the DFS configuration file of the search headcaptain, those indexers will quickly be configured to launch workernodes because the search head captain will replicate the DFSconfiguration file for all the search heads 226, such that all thesearch heads 226 will have a copy of the updated list of activeindexers.

Once the search head captain has distributed the updated DFSconfiguration file to the other search heads 226 in the cluster 228, thesearch head captain may also distribute (e.g., indirectly) a copy of theupdated DFS configuration file to each of the indexers 230. In someimplementations, the search head captain may be able replicate anddistribute the DFS configuration file to all the indexers 230 using anexisting mechanism in the system, such as a distributed bundledreplication mechanism. The distributed bundle replication mechanism mayinvolve a communication technique used by a search head for processing asearch query through a set of indexers, in which the search head mayprovide a bundle of configuration information specific to the searchhead to the set of indexers, such that the indexers are aware of thesearch head and the configuration settings to be applied during thesearch. This bundle of configuration information may include the DFSconfiguration file (e.g., containing information for the DFS master 234,the list of active indexers, relevant security information, etc.) aspart of the bundle, as well as other information including knowledge ofobjects, labels (e.g., how a field is renamed in the search), serverconfiguration information, license check information, and so forth. Inorder for the search head captain to leverage this distributed bundlereplication mechanism, the search head captain may trigger an ad hoc(e.g., a “dummy” or fake) DFS search, for which the all the indexers 230in the system 224 will be contacted by the search head that handles thesearch (e.g., a comprehensive search). The search head captain will havethat search head push a bundle of configuration information (includingthe DFS configuration file available to the search head) to everyindexer 230 for performing the search. Since all of the search heads 226will have been provided the updated DFS configuration file at this point(including the search head handling this fake DFS search), it is ensuredthat the updated DFS configuration will be in the bundle ofconfiguration information provided to every indexer 230 for the fake DFSsearch. Thus, using the distributed bundle replication mechanism, thesearch head captain is able to indirectly distribute the updated DFSconfiguration file to all the indexers 230 in the system 224.

It should be noted that after an indexer 230 has received a DFSconfiguration file from a search head, the indexer 230 and/or the daemon232 running on the indexer 230 may interchangeably fulfill variousresponsibilities and perform tasks described herein. However, for thepurpose of facilitating ease of understanding, these tasks will bedescribed as being performed by the indexer 230, but it should beunderstood that any suitable component, software, or process of theindexer 230 may be initiating the performance of these tasks.

With respect to these tasks, the indexer 230 may specifically monitorreceipt of any DFS configuration files. For instance, the daemon 232running on the indexer 230 may be continually monitoring to see if a newbundle of configuration information has been received. If a bundle ofconfiguration information has been received, the contents of the bundlemay be checked to determine if it includes a DFS configuration file.From the contents of an updated DFS configuration file, the indexer 230will know about the DFS master 234, the shared secret key, the currentlist of active indexers (e.g., the selected indexers qualified tooperate worker nodes), and so forth. In particular, the list of activeindexers will be used by the indexers 230 to determine which of theindexers among them (e.g., from the IP address, hostname, GUID, etc.,listed for those indexers) will have worker nodes instantiated for thesearch. The indexer 230 may continually monitor for receipt of a newbundle of configuration information, because the search head captain maycontinue to make updates to the DFS configuration file as changes aremade (e.g., an indexer is added or removed from the list of activeindexers) or in the event that a new search head captain arises.

Once an indexer 230 has been provided with an updated DFS configurationfile, the indexer 230 may check to see if it is on the list of activeindexers (e.g., the selected indexers qualified to operate worker nodes)provided in the DFS configuration file. If the indexer 230 is not on thelist of active indexers, the indexer 230 may do nothing. If the indexer230 is on the list of active indexers, it may launch and activate aworker node (e.g., by running the worker node software) and configurethe worker node to connect to the DFS master 234 using the availableinformation for the DFS master 234 (including connection information forthe DFS master 234, such as IP address) and the shared secret key, bothof which were included in the updated DFS configuration file. The workernode can use this information to connect to the DFS master 234 and theconnection will be successful because the worker node and the DFS master234 will share the same secret key. Thus, the shared secret key mayserve as a security mechanism for ensuring that worker nodes (e.g.,rogue worker nodes) that are not part of the system 224 cannot connectto the DFS master 234 for search processing. In some cases, it is thedaemon 232 of the indexer 230 that makes this determination of whetherto launch and activate a worker node on the indexer 230, triggersexecution of the installed worker node software, and configures theworker node to communicate with the DFS master 234. In someimplementations, a user may be able to configure and assign a resourcequota for this activated worker node operating on the indexer 230. Forinstance, the user may be able to configure how resources, including CPUand memory, may be allocated for this activated worker node operating onthe indexer 230. Resources may be allocated as an absolute resourceamount, or as a percentage of total resources associated with theindexer 230. The resource usage and allocation of the worker nodeoperating on the indexer 230 may be continually monitored, and resourcesmay be assigned to the worker node in accordance with a workloadmanagement policy, which may be set per indexer (e.g., or per workernode). For instance, a user may specify that the indexer 230 mayallocate X % of an indexer resource for indexing, Y % of the resourcefor searching, and Z % of the resource for operating the worker node.

In various implementations, there may be additional security mechanismsin place. In some implementations, the DFS master 234 may maintain anawareness of the worker nodes that successfully connected to it with theshared secret key, the corresponding indexers on which those workernodes are instantiated, and/or the total number of indexers 230 in thesystem 224. When performing a DFS search, a check can be made to see ifthe number of worker nodes connected to the DFS master 234 exceeds thenumber of indexers available. If the number of worker nodes is greaterthan the number of indexers, then the DFS search is not performed (e.g.,because there may be a rogue worker node). Another check may also bemade to determine that the worker nodes connected to the DFS master 234correspond to actual indexers 230 in the system 224. For instance, theremay be a mapping table that maps the relationship between worker nodesand their corresponding indexers (e.g., worker node identifiers andcorresponding indexer identifiers). If each connected worker node doesnot correspond to the correct indexer, then the DFS search is notperformed.

In various implementations, for which a multi-site deployment is used,there may be a location preference that is assessed during theconfiguration and management of the components of the distributedcomputing framework. In a multi-site deployment, the physical locationsof individual components (e.g., the search heads 226 and indexers 230)may be different. For example, there may be a first set of search headsand indexers that are located on the east coast, with a second set ofsearch heads and indexers that are located on the west coast. In thisexample, the search head captain may be one of the search heads on theeast coast. If the underlying data for a DFS search is stored in theeast coast, then the location preference may dictate that the only eastcoast worker nodes (e.g., worker nodes instantiated on indexers locatedon the east coast) should be used as part of the DFS search. In thiscase, even if the list of active indexers contains both east coastindexers and west coast indexers, only the worker nodes running on theeast coast indexers may be used. Thus, there may be a locationpreference for using worker nodes that are in the same geographicallocation as the underlying data being processed for a DFS search. Inanother example scenario, if the data for the DFS search was splitbetween the east coast and the west coast, then worker nodes in bothlocations can be used to process the DFS search. However, each site mayperform as much of the search processing as it can in order to minimizethe amount of information that would have to be passed between the sitesover the wide area network (WAN). For instance, if the search processinginvolved the determination of a metric (e.g., that is associative,commutative, and can be aggregated) such as a count, then the workernodes on the east coast indexers may process the east coast data for acount, the worker nodes on the west coast indexers may process the westcoast data for a count, and the count between the two sites can beadded.

7.1. Co-Located Deployment Operations

FIG. 26A is an operation flow diagram illustrating an example of anoperation flow of a co-located deployment of a DFS system with a dataintake and query system according to some embodiments of the presentdisclosure. The operational flow 2800 shows the processes forestablishing the co-located DFS system and search operations carried outin the context of the co-located deployment.

In step 2802, a search head of the cluster 228 can launch the DFS master234 and/or launch a connection to the DFS master 234. For example, asearch head can use a modular input to launch an open source DFS master234. Moreover, the search head can use the modular input to launch amonitor of the DFS master 234. The modular input can be a platformadd-on of the data intake and query system that can be accessed in avariety of ways such as, for example, over the Internet on a networkportal.

In step 2804, the peer indexers 230 can launch worker nodes 236. Forexample, each peer indexer 230 can use a modular input to launch an opensource worker node. In some embodiments, only some of the peer indexers230 launch worker nodes, which results in a topology where not all ofthe peer indexers 230 have an associated worker node. Moreover, the peerindexers 206 can use the modular input to launch a monitor of the workernodes 236.

In step 2806, the cluster 228 can launch one or more instances of a DFSservice. For example, any or each of the search heads of the cluster 228can launch or communicate with an instance of the DFS service. Hence,the co-located deployment can launch and use multiple instances of a DFSservice but need only launch and use a single DFS master 234. In theevent that a launched DFS master fails, the lead search head using themonitoring modular input can restart the failed DFS master. However, ifthe DFS master fails along with the lead search head, another searchhead can be designated as the cluster 228's leader and can re-launch theDFS master.

In step 2808, a search head of the cluster 228 can receive a searchquery. For example, a search query may be input by a user on a userinterface of a display device. In another example, the search query canbe input to the search head in accordance with a scheduled search.

In step 2810, a search head of the cluster 228 can initiate a DFS searchsession with the local DFS service. For example, any of the membersearch heads of the cluster 228 can receive a search query and, inresponse to the search query, a search head can initiate a DFS searchsession using an instance of the DFS service.

In step 2812, a search head of the cluster 228 (or a search serviceprovider) triggers a distributed search on the peer indexers 230 if thesearch query requires doing so. In other words, the search query isapplied on the peer indexers 230 to collect partial search results frominternal data stores (not shown).

In step 2814, the distributed search operations continue with the peerindexers 230 retrieving partial search results from internal datastores, and transporting those partial search results to the workernodes 236. In some embodiments, the internal partial search results arepartially reduced (e.g., combined), and transported by the peer indexers230 to their respective worker nodes 236 in accordance with parallelexporting techniques. In some embodiments, if each peer indexer does nothave an associated worker node, the peer indexer can transfer itspartial search results to the nearest worker node in the topology ofworker nodes. In step 2816, the worker nodes 236 collect the partialsearch results extracted from the external data sources 240.

In step 2818, the worker nodes 236 can aggregate (e.g., merge andreduce) the partial search results from the internal data sources andthe external data sources 240. For example, the aggregation of thepartial search results may include combining the partial search resultsof indexers 230 and/or the external data stores 240. Hence, the workernodes 236 can aggregate the collective partial search results at scalebased on DFS native processors residing at the worker nodes 236.

In some embodiments, the aggregated partial search results can be storedin memory at worker nodes before being transferred between other workernodes to execute a multi-staged parallel aggregation operation. Onceaggregation of the partial search results has been completed (e.g.,completely reduced) at the worker node 236, the aggregated partialsearch results can be read by the DFS service running locally to thecluster 228. For example, the DFS service can commence reading theaggregated search results as event chunks.

In step 2820, the aggregate partial search results read by the DFSservice are transferred to the DFS master 234 or search serviceprovider. Then, in step 2822, the DFS master 234 can transfer the finalsearch results to the cluster 228. For example, the aggregated partialsearch results can be transferred by the worker nodes 236 as eventchunks at scale to the DFS master 234, which can transfer search results(e.g., those received or derived therefrom) to the lead search headorchestrating the DFS session.

Lastly, in step 2822, a search head can cause the search results or dataindicative of the search results to be rendered on user interface of adisplay device. For example, the search head member can make the searchresults available for visualizing on a user interface rendered on thedisplay device.

It will be understood that fewer or more, or different steps can beincluded in the operation flow 2800. Further, some operations can beperformed by different components of the system. In some embodiments,for example, some of the tasks described as being performed by thesearch head 210 can be performed by the search service 220, such as thesearch service provider 216. In some cases, step 2806 can be omitted. Insome cases, upon determining that a search query is to be handled by thesearch service, the cluster 228 can communicate the query to the searchservice. In turn, the search service can trigger the distributed search,etc.

FIG. 26B is an operation flow diagram illustrating an example of anoperation flow of a co-located deployment of a DFS system according tosome embodiments of the present disclosure. Within the context providedby FIG. 26A, the operational flow 2830 of FIG. 26B shows the processesfor establishing and managing the components used in a distributedcomputing framework (e.g., steps 2802, 2804, and 2806 of the operationalflow 2800 of FIG. 26A). Prior to the steps shown in the operational flow2830, software for the DFS manager 234 may be installed on the searchheads 226, software for the worker nodes 236 may be installed on theindexers 230, and there may be a daemon 232 that runs on each of thesearch heads 226 and indexers 230.

In step 2832, the search heads 226 of the cluster 228 may identify,among themselves, a search head captain (e.g., search head captain226-1). Any suitable method of selecting a search head to serve as thesearch head captain may be used, including the example described hereinfor the election of one of the search heads to be the search headcaptain based on the votes of all the search heads 226 (e.g., using aRaft consensus algorithm).

In step 2834, the DFS manager 234 may be launched on the search headcaptain 226-1. For example, the daemon 232 running on the search headselected as search head captain from step 2832 may be aware of thechange in search head captain status and trigger the execution of thesoftware for the DFS manager 234 installed on the search head. Oncelaunched, this instance of the DFS master 234 will run on the searchhead captain 226-1 until the status of the search head captain 226-1changes (e.g., it fails or is no longer the captain), but there shouldalways be an instance of DFS master 234 in the system that is running onthe search head captain for that particular point in time.

In step 2836, the DFS manager 234 or the search head captain 226-1 maydetermine a list of active indexers, which is a list of indexers (e.g.,the identifiers associated with those indexers) that are qualified tolaunch and run worker nodes. The list of active indexers may, in someinstances, be referred to as a list of active workers, since all theindexers in this list may be registered as workers (either currentlyconfigured, or in the process of being configured). In someimplementations, a user may select the list of active indexers andprovide the list to the DFS manager 234. For instance, there may be aweb-based UI that enables a user to interface with the DFS manager 234and configure the list of active indexers. In some cases, the user maybe able to select indexers from among all the available indexers to addto the list of active indexers, based on criteria that may be ofrelevance to the user (e.g., indexer capacity, and so forth).

In step 2838, the DFS manager 234 or the search head captain 226-1 maygenerate a shared secret key that will be provided to the worker nodes236 for security purposes.

In step 2840, the DFS manager 234 or the search head captain 226-1 mayupdate the DFS configuration file on the search head captain 226-1. Thisupdated DFS configuration file will include information associated withthe instance of the DFS master 234 running on the search head captain226-1 (e.g., an instance identifier, an IP address or port associatedwith the DFS master 234, etc.), the shared secret key, and the list ofactive indexers.

In step 2842, the DFS manager 234 or the search head captain 226-1 mayreplicate and distribute the updated DFS configuration file to all ofthe other search heads 226. The updated DFS configuration file will thenbe integrated by those other search heads 226 (e.g., saved on each ofthe search heads 226). In some cases, the daemon 232 operating on eachof those search heads 226 may be configured to continually monitor forthis updated DFS configuration file and integrate it when received.

In step 2844, the DFS manager 234 or the search head captain 226-1 mayinitiate an ad hoc search (e.g., a fake search) with the search heads226 that would involve all the indexers 230 in the system, in order toindirectly distribute the updated DFS configuration file to all theindexers 230.

In step 2846, the search heads 226 may distribute the updated DFSconfiguration file to all the indexers 230 as part of the ad hoc search(e.g., a copy of the updated DFS configuration file that each of thesearch heads 226 have after step 2842). The updated DFS configurationfile may be distributed with other information, such as a bundle ofconfiguration information typically used to configure the indexers 230for a search query.

In step 2848, each of the indexers 230 or the daemon 232 running onthose indexers 230 may receive the updated DFS configuration file andcheck to determine if the indexer is part of the list of active indexersin the received DFS configuration file. This can be done as part of acontinual and ongoing process since the indexers 230 may expect toreceive an updated DFS configuration file at any time (e.g., in theevent the current search head captain crashes, the list of activeindexers changes, and so forth)

In step 2850, the daemon 232 for the indexers 230 that are in the listof active indexers may launch and activate instances of worker nodes 236on those active indexers. The worker nodes 236 may be configured withinformation needed for them to connect to the DFS master 234, such asthe connection information for the instance of the DFS master 234 andthe shared secret key generated by that instance of the DFS master 234,which was provided in the DFS configuration file.

In step 2852, the worker nodes 236 may use the information and attemptto connect to the DFS master 234 to become part of the distributedcomputing framework. The connection to the DFS master 234 should besuccessful if the shared secret key provided to the worker nodes 236 arethe same as the shared secret key generated by the instance of the DFSmaster 234 at step 2838.

FIG. 26C is a flow diagram illustrative of an embodiment of a routineimplemented by a co-located deployment of a DFS system to activateworker nodes of a distributed computing framework using a search commandaccording to some embodiments of the present disclosure. Morespecifically, the routine 2860 may be associated with the establishmentand management of components of a distributed computing framework, whichmay require the synchronization of configuration information among thosecomponents. The activation of worker nodes through a search command maybe one way in which components of the distributed computing frameworkcan be synchronized using existing techniques and protocols available toa data intake and query system. Although described as being implementedby an indexer, it will be understood that the elements outlined forroutine 2860 can be implemented by one or more computingdevices/components that are associated with the data intake and querysystem 108, such as, but not limited to, the indexing manager 402, theingest manager 406, the partition manager 408, the indexer 410, and soforth. Thus, the following illustrative embodiment should not beconstrued as limiting.

At block 2862, a computing device (e.g., an indexer) of a data intakeand query system may receive a search command from a search head of thedata intake and query system. In some cases, the search command may beassociated with an ad hoc search initiated by a search captain.

At block 2864, a configuration file associated with the received searchcommand may also be received. This configuration file may be received bythe computing device (e.g., the indexer). The configuration file mayinclude information regarding a distributed computing framework (e.g.,SPARK) associated with the data intake and query system. For instance,the configuration file may be a DFS configuration file that includesinformation associated with a DFS master, a shared secret key, and alist of active indexers that are qualified to operate worker nodes. Insome cases, this configuration file may also be received from the searchhead, and it may be received along with the search command. In somecases, the search head sending the configuration file may havepreviously received the configuration file (or at least the relevantinformation contained within the configuration file) from another searchhead, such as a search head captain.

At block 2866, a determination is made regarding whether theconfiguration file specifically identifies the computing device (e.g.,the indexer). The computing device (e.g., the indexer) may make thisdetermination itself. For instance, the configuration file may include alist of active indexers that are qualified to operate a worker node inthe distributed computing framework, and the computing device may checkthat it is in the list of active indexers. Thus, a determination is maderegarding whether the configuration file identifies the computing deviceto include a worker node of the distributed computing framework.

At block 2868, a particular worker node of the distributed computingframework is activated on the computing device (e.g., the indexer) if itis determined that the configuration file does identify the computingdevice to include a worker node of the distributed computing framework.The computing device may launch and active this particular worker nodeitself. In some cases, the particular worker node may be configuredusing information in the received configuration file, and the particularworker node may use that information to connect to the DFS master andbecome part of the distributed computing framework.

It should be noted that, in some cases, additional indexers may be addedto the system, on which additional worker nodes can be instantiated on.Some of such indexers may be configured to dedicate/allocate a majorityor all of their capacity or resources for operating worker nodes used aspart of the distributed computing framework.

8.0. Cloud Deployment Architecture

The performance and flexibility of a data intake and query system havingcapabilities extended by a DFS system can be improved with deployment ona cloud computing platform. For example, FIG. 27 is a cloud-based systemdiagram illustrating a cloud deployment of a DFS system in which anembodiment may be implemented.

In particular, a cloud computing platform can share processing resourcesand data in a multi-tenant network. As such, the platform's computingservices can be used on demand in a cloud deployment of a DFS system.The platform's ubiquitous, on-demand access to a shared pool ofconfigurable computing resources (e.g., networks, servers, storage,applications, and services), which can be rapidly provisioned andreleased with minimal effort, can be used to improve the performance andflexibility of a data intake and query system extended by a DFS system.

In the illustrated embodiment, a cloud-based system 242 includescomponents of a data intake and query system extended by the DFS systemimplemented on a cloud computing platform. However, the cloud-basedsystem 242 is shown with only some components of a data intake and querysystem in a cloud deployment but can include other components (e.g.,forwarders) that have been omitted for brevity. As such, the componentsof the cloud-based system 242 can be understood by analogy to otherembodiments described elsewhere in this disclosure.

An example of a suitable cloud computing platform include Amazon webservices (AWS), which includes elastic MapReduce (EMR) web services.However, the disclosed embodiments are not so limited. Instead, thecloud-based system 242 could include any cloud computing platform thatuses EMR-like clusters (“EMR clusters”).

In particular, the cloud-based system 242 includes a search head 244 asa tenant of a cloud computing platform. Although shown with only thesearch head 244, the cloud-based system 242 can include any number ofsearch heads that act independently or collectively in a cluster. Thesearch head 244 and other components of the cloud-based system 242 canbe configured on the cloud computing platform.

The cloud-based system 242 also includes any number of worker nodes 246as cloud instances (“cloud worker nodes 246”). The cloud worker nodes246 can include software modules 248 running on hardware devices of acloud computing platform. The software modules 248 of the cloud workernodes 246 are communicatively coupled to a search service (e.g.,including a DFS master 250 or search service provider), which iscommunicatively coupled to a daemon 252 of the search head 244 tocollectively carry out operations of the cloud-based system 242.

The cloud-based system 242 includes index cache components 254. Theindex cache components 254 are communicatively coupled to cloud storage256, which can form a global index 258. The index cache components 254are analogous to indexers, and the cloud storage 256 is analogous tointernal data stores described elsewhere in this disclosure. The indexcache components 254 are communicatively coupled to the cloud workernodes 246, which can collect partial search results from the cloudstorage 256 by applying a search query to the index cache components254.

Lastly, the cloud worker nodes 246 can be communicatively coupled to oneor more external data sources 260. In some embodiments, only some of thecloud worker nodes 246 are coupled to the external data sources 260while others are only coupled to the index cache components 254. Forexample, the cloud worker nodes 246-1 and 246-3 are coupled to both theexternal data sources 260 and the index cache component 254, while thecloud worker node 246-2 is coupled to the index cache component 254-1but not the external data sources 260.

The scale of the cloud-based system 242 can be changed dynamically asneeded based on any number of metrics. For example, the scale can changebased on pricing constraints. In another example, the scale of the EMRcluster of nodes can be configured to improve the performance of searchoperations. For example, the cloud-based system 242 can scale the EMRcluster depending on the scope of a search query to improve theefficiency and performance of search processing.

In some embodiments, the EMR clusters can have access to flexible datastores such as a Hadoop distributed file system (HDFS), Amazon simplestorage services (S3), NoSQL, SQL, and custom SQL. Moreover, in someembodiments, the cloud-based system 242 can allow for a sharded query ofdata within these flexible data stores in a manner which makes scalingand aggregating partial search results (e.g., merging) most efficientwhile in place (e.g., reduces shuffling of partial search resultsbetween cloud worker nodes).

8.1. Cloud Deployment Operations

FIG. 28 is a flow diagram illustrating an example of a method 3000performed in a cloud-based DFS system (“cloud-based system”) accordingto some embodiments of the present disclosure. The operations of thecloud-based system are analogous to those described elsewhere in thisdisclosure with reference to other embodiments and, as such, a personskilled in the art would understand those operations in the context of acloud deployment. Accordingly, a description of the flow diagramhighlights some distinctions of the cloud deployment over otherembodiments described herein.

In step 3002, the search head of the cloud-based system receives asearch query. In step 3004, the cloud-based system determines the typeof EMR cluster to use based on the scope of the received search query.For example, the cloud-based system can support two different types ofEMR clusters. In a first type scenario, a single large EMR cluster couldbe used for all search operations. In a second type scenario, subsets ofsmaller EMR clusters can be used for each type of search load. That is,a smaller subset of an EMR cluster can be used for a less complexaggregation processing of partial search results from different datasources. In some embodiments, the scale of an EMR cluster for the firstor second type can be set for each search load by a user or based on arole quota. In other words, the scale of the EMR cluster can depend onthe user submitting the search query and/or the user's designated rolein the cloud-based system.

In step 3006, the cloud-based system is dynamically scaled based on theneeds determined from the received search query. For example, the searchheads or cloud worker nodes can be scaled under the control of a searchservice to grow or shrink as needed based on the scale of the EMRcluster used to process search operations.

In step 3008, the cloud worker nodes can collect the partial searchresults from various data sources. Then, in step 3010, the cloud workernodes can aggregate the partial search results collected from thevarious data sources. Since the cloud worker nodes can scaledynamically, this allows for aggregating (e.g., merging) partial searchresults in an EMR cluster of any scale.

In step 3012, the resulting aggregated search results can be computedand reported at scale to the search head or search service provider.Thus, the cloud-based system can ensure that data (e.g., partial searchresults) from diverse data sources (e.g., including time-indexed eventswith raw data or other type of data) are reduced (e.g., combined) atscale on each EMR node of the EMR cluster before sending the aggregatedsearch results to the search head or search service provider.

The cloud-based system may include various other features that improveon the data intake and query system extended by the DFS system. Forexample, in some embodiments, the cloud-based system can collect metricswhich can allow for a heuristic determination of spikes in DFS searchrequirements. The determination can also be accelerated throughauto-scaling of the EMR clusters.

In some embodiments, the cloud-based system can allow DFS apps of thedata intake and query system to be bundled and replicated over an EMRcluster to ensure that they are executed at scale. Lastly, thecloud-based system can include mechanisms that allow user- orrole-quota-honoring based on a live synchronization between the dataintake and query system user management features and a cloud accesscontrol features.

9.0. Timeline Visualization

The disclosed embodiments include techniques for organizing andpresenting search results obtained from within a big data ecosystem viaa data intake and query system. In particular, a data intake and querysystem may cause output of the search results or data indicative of thesearch results on a display device. An example of a display device isthe client device 22 shown in FIG. 1A connected to the data intake andquery system 16 over the network 33.

For example, the data intake and query system 16 can receive a searchquery input by a user at the client device 22. The data intake and querysystem 16 can run the query on distributed data systems to obtain searchresults. The search results are then communicated to the client device22 over the network 33. The search results can be rendered in a visualway on the display of the client device 22 using items such as windows,icons, menus, and other graphics or controls.

For example, a client device can run a web browser that renders awebsite, which can grant a user access to the data intake and querysystem 16. In another example, the client device can run a dedicatedapplication that grants a user access to the data intake and querysystem 16. In either case, the client device can render a graphical userinterface (GUI), which includes components that facilitate submittingsearch queries, and facilitate interacting with and interpreting searchresults obtained by applying the submitted search queries on distributeddata systems of a big data ecosystem.

The disclosed embodiments include a timeline tool for visualizing thesearch results obtained by applying a search query to a combination ofinternal data systems and/or external data systems. The timeline toolincludes a mechanism that supports visualizing the search results byorganizing the search results in a time-ordered manner. For example, thesearch results can be organized into graphical time bins. The timelinetool can present the time bins and the search results contained in oneor more time bins. Hence, the timeline tool can be used by an analyst tovisually investigate structured or raw data events which can be ofinterest to the analyst.

The timeline mechanism supports combining timestamped andnon-timestamped search results obtained from diverse data systems topresent a visualization of the combined search results. For example, asearch query may be applied to the external data systems that each usedifferent compute resources and run different execution engines. Thetimeline mechanism can harmonize the search results from these datasystems, and a GUI rendered on a display device can present theharmonized results in a time-ordered visualization.

FIG. 29 is a flowchart of a method 3100 for illustrating a timelinemechanism that supports rendering search results in a time-orderedvisualization according to some embodiments of the present disclosure.For example, the search head can dictate to the DFS master whether acursored or reporting search should be executed, or a search serviceprovider can make this determination. The search service provider candefine a search scheme and/or search process and create a DAG. The DAGcan orchestrate the search operations performed by the worker nodes forthe cursored or reporting search.

In step 3102, the search service receives an indication that a requestfor a timeline visualization was received by the data intake and querysystem. For example, a user may input a request for a timelinevisualization before, after, or when a search query is input at a clientdevice. In another example, the data intake and query systemautomatically processes time-ordered requests to visualize in a timeline

In step 3104, the search service determines whether the requestedvisualization is for the search results of a cursored search or atime-ordered reporting search. For example, a cursored search may queryindexers of the data intake and query system as well as external datastores for a combination of time ordered partial search results. Inanother example, a time-ordered reporting search may require queryingthe indexers and external data stores for a time-ordered statistic basedon the combination of time ordered partial search results.

The search results for the timeline tool can be obtained in accordancewith a “Fast,” “Smart,” or “Verbose” search mode depending on whether acursored search or a reporting search was received. In particular, acursored search supports all three modes whereas a reporting search mayonly support the Verbose mode. The Fast mode prioritizes performance ofthe search and does not return nonessential search results. This meansthat the search returns what is essential and required. The Verbose modereturns all of the field and event data it possibly can, even if thesearch takes longer to complete, and even if the search includesreporting commands. Lastly, the default Smart mode switches between theFast and Verbose modes depending on the type of search being run (e.g.,cursored or reporting).

In step 3106, if the search is a cursored search, the search servicecreates buckets for the search results obtained from distributed datasystems. The buckets are created based on a timespan value. The timespanvalue may be a default value or a value selected by a user. For example,a timespan value may be 24 hours. The buckets may each represent adistinct portion of the timespan. For example, each bucket may representa distinct hour over a time-span of 24 hours.

The number of buckets that are created may be a default value dependingon the timespan, or depending on the number of data systems from whichsearch results were collected. For example, a default number of buckets(e.g., 1,000 buckets) may be created to span a default or selectedtimespan. In another example, distinct and unique buckets are createdfor portions of the timespan. In another example, a unique bucket iscreated per data system. In yet another example, buckets are created forthe same portion of the timespan but for different data systems.

In step 3108, search results obtained by application of the search queryto the different data systems are collected into the search buckets. Forexample, each bucket can collect the partial search results fromdifferent data systems that are timestamped with values within the rangeof the bucket. As such, the buckets support the timeline visualizationby organizing the search results.

In step 3110, the search service transfers a number of search resultscontained in the buckets to the search head. However, the search servicemay need to collect all the search results from across the data systemsinto the buckets before transferring the search results to the searchhead to ensure that the timeline visualization is rendered accurately.Moreover, the search results of the bucket may be transferred from thebuckets in chronological order. For example, the contents of the bucketsrepresenting beginning of the timespan are transferred first, and thecontents of the next buckets in time are transferred next, and so on.

In some embodiments, the number of search results transferred to thesearch head from the buckets may be a default or maximum value. Forexample, the first 1,000 search results from the buckets at thebeginning of the timespan may be first transferred to the search headfirst. In some embodiments, the search service transfer a maximum numberof search results per bin to the search head. In other words, the numberof search results transferred to the search head corresponds to themaximum number that can be contained in one or more bin of the timelinevisualization. Lastly, in step 3112, the search results of the reportingsearch received by the search head from the buckets are rendered in atimeline visualization.

In step 3114, if the search is a time-ordered reporting search, thesearch service creates buckets for the search results obtained fromdistributed data systems. The buckets can be created based on the numberof shards or partitions from which the search results are collected.

In step 3116, the search results are collected from across thepartitions. For external data sources, partial search results (e.g.,treated as raw events) are collected from across the shards/partitionsin time-order and transferred to the timeline mechanism. In case ofexternal data systems which have the capability to support shardedpartitions, multiple worker nodes can request for each specific shard orpartition. If needed, each partition can be sorted based on userspecified constraints such as, for example, a time ordering constraint.For sorting purposes, sometimes instead of unique shards, the DFS systemcan provide overlapping shards. For overlapping buckets across multipledata sources, the search service may need to collect partial searchresults across the different data sources before sending search resultsto the search head.

In step 3118, the search service transfers a number of search resultscontained in the buckets to the search head. However, the search servicemay need to collect all the search results from across the data systemsinto the buckets before transferring the search results to the searchhead to ensure that the timeline visualization is rendered accurately.Moreover, the search results of the bucket may be transferred from thebuckets in chronological order. For example, the contents of the bucketsrepresenting beginning of the timespan are transferred first, and thecontents of the next buckets in time are next, and so on.

In some embodiments, the number of search results transferred to thesearch head from the buckets may be a default or maximum value. Forexample, the first 1,000 search results from the buckets at thebeginning of the timespan may be first transferred to the search headfirst. In some embodiments, the search service transfers a maximumnumber of search results per bin to the search head. In other words, thenumber of search results transferred to the search head corresponds tothe maximum number that can be contained in one or more bin of thetimeline visualization. Lastly, in step 3120, the search results of thereporting search received by the search head from the buckets arerendered in a timeline visualization.

FIG. 30 illustrates a timeline visualization rendered on a userinterface 62 in which an embodiment may be implemented. The timelinevisualization presents event data obtained in accordance with a searchquery submitted to a data intake and query system. In the illustratedembodiment, the search query is input to search field 64 using SPL, inwhich a set of inputs is operated on by a first command line, and then asubsequent command following the pipe symbol “|” operates on the resultsproduced by the first command, and so on for additional commands. Asshown, a command on the left of the pipe symbol can set the scope of thesearch, which could include external data systems. Other commands on theright of the pipe symbol (and subsequent pipe symbols) can specify afield name and/or statistical operation to perform on the data sources.

In some embodiments, the search head or search service provider canimplement specific mechanism to parse the SPL. The search head or searchservice provider can determine that some portion of the search query isto be executed on the worker nodes base on the scope of the searchquery. In some embodiments, the search query can include a specificsearch command that triggers the search head to realize which portion ofthe search query should be executed by the DFS system. As a result, thephase generator can define the search phases, and where each of thosephases will be executed. In addition, once the phase generator decidesan operation needs to be executed by the DFS system, the search head orsearch service provider can optimize to push as much of the searchoperation as possible, for example, first to the external data sourceand then to the DFS system. In some embodiments, only the commands notincluded in the DFS command set will be executed back on the search heador search service provider once the results are retrieved to the searchhead or search service provider.

The timeline visualization presents multiple dimensions of data in acompact view, which reduced the cognitive burden on analysts viewing acomplex collection of data from internal and/or external data systems.That is, the timeline visualization provides a single unified view tofacilitate analysis of events stored across the big data ecosystem.Moreover, the timeline visualization includes selectable components tomanipulate the view in a manner suitable for the needs of an analyst.

The timeline visualization includes a graphic 66 that depicts a summaryof the search results in a timeline lane (e.g., in the form of rawevents), as well as a list of the specific search results 68. As shown,the timeline summary of the search results are presented as rectangularbins that are chronologically ordered and span a period of time (e.g.,Sep. 5, 2016 5:00 PM through Sep. 6, 2016 3:00 PM). The height of a binrepresents the magnitude of the quantity of events in that grouprelative to another group arranged along the timeline. As such, theheight of each bin indicates a count of events for a subset of theperiod of events relative to other counts for other bins within theperiod of time. The events in a group represented by a bin may have atimestamp value included in the range of time values of thecorresponding bin. Below the timeline summary is a listing of events ofthe search results presented in chronological order.

FIG. 31 illustrates a selected bin 70 of the timeline visualization andthe contents of the selected bin 70 according to some embodiments of thepresent disclosure. Specifically, the timeline visualization may includegraphic components that enable an analyst to investigate additionaldimensions of the search results summarized in the timeline. As shown,each bin representing a group of events may be selectable by an analyst.Selecting a bin may cause the GUI to display the specific group ofevents associated with the bin in the list below the timeline summary.Specifically, selecting a bin may cause the GUI to display the events ofthe search results that are timestamped within a range of thecorresponding group.

The timeline visualization is customizable and adaptable to presentsearch results in various convenient manners. For example, a user canchange the ordering of groups of events to obtain a differentvisualization of the same groups. In another example, a user can changethe range of the timeline to obtain a filtered visualization of thesearch results. In yet another example, a user can hide some events toobtain a sorted visualization of a subset of the search results.

In some embodiments, the activity for each data system may appear in aseparate timeline lane. If an activity start-time and duration areavailable for a particular data system, the respective timeline may showa duration interval as a horizontal bar in the lane. If a start time isavailable, the timeline visualization may render an icon of that time onthe visualization. As such, the timeline visualization can be customizedand provide interactive features to visualize search results, andcommunicate the results in dashboards and reports.

Thus, the timeline visualization can support a timeline visualization ofexternal data systems, where each external data system may operate usingdifferent compute resources and engines. For example, the timelinevisualization can depict search results obtained from one or moreexternal data systems, collated and presented in a single and seamlessvisualization. As such, the timeline visualization is a tool ofunderlying logic that facilitates investigating events obtained from anyof the external data systems, internal data systems (e.g., indexers), ora combination of both.

The underlying logic can manage and control the timeline visualizationrendered on the GUI in response to data input and search resultsobtained from within the big data ecosystem. In some embodiments, theunderlying logic is under the control and management of the data intakeand query system. As such, an analyst can interface with the data intakeand query system to use the timeline visualization. For example, thetimeline logic can cause the timeline visualization to render activitytime intervals and discrete data events obtained from various datasystem resources in internal and/or external data systems.

The underlying logic includes the search service. Since the bins mayinclude events data from multiple data systems, each bin can representan overlapping bin across multiple data systems. Accordingly, the searchservice can collect the data events across the different data systemsbefore sending them to the search head. To finalize a search operation,the search service may transmit the maximum number of events per bin orthe maximum size per bin to the search head.

In some embodiments, the underlying logic uses the search head of thedata intake and query system to collect data events from the variousdata systems that are presented on the timeline visualization. In someembodiments, the events are collected in accordance with any of themethods detailed above, and the timeline visualization is a portal forviewing the search results obtained by implementing those methods. Assuch, the collected events can have timestamps indicative of, forexample, times when the event was generated.

The timestamps can be used by the underlying logic to sort the eventsinto the bins associated with any parameter such as a time range. Forexample, the underlying logic may include numerous bins delineated byrespective chronological time ranges over a total period of time thatincludes all the bins. In some embodiments, a maximum amount of eventstransferred into the time bins could be set.

In some embodiments, the underlying logic of the timeline visualizationcan automatically create bins for a default timespan in response tocursored searches of ordered data. For example, an analyst may submit acursored search, and the underlying logic may cause the timelinevisualization to render a display for events within a default timespan.The amount and rate at which the events are transferred to the searchhead for subsequent display on the timeline visualization could varyunder the control of the underlying logic. For example, a maximum numberof events could be transferred on a per bin basis by the worker nodes tothe search head. As such, the DFS system could balance the load on thenetwork.

In some embodiments, the underlying logic of the timeline visualizationcan utilize the sharding mechanism detailed above for reporting searchesof ordered data from external data systems. Specifically, the data couldbe sharded across partitions in response to a reporting search, whereexecutors have overlapping partitions. Further, the underlying logic maycontrol the search head or search service provider to collect the eventsdata across the shards/partitions in time order for rendering on thetimeline visualization. Under either the cursored search or reportingsearch, the underlying logic may impose the maximum size of total eventstransferred into bins.

10.0. Monitoring and Metering Services

The disclosed embodiments also include monitoring and metering servicesof the DFS system. Specifically, these services can include techniquesfor monitoring and metering metrics of the DFS system. The metrics arestandards for measuring use or misuse of the DFS system. Examples of themetrics include data or components of the DFS system. For example, ametric can include data stored or communicated by the DFS system orcomponents of the DFS system that are used or reserved for exclusive useby customers. The metrics can be measured with respect to time orcomputing resources (e.g., CPU utilization, memory usage) of the DFSsystem. For example, a DFS service can include metering the usage ofparticular worker nodes by a customer over a threshold period of time.

In some embodiments, a DFS service can meter the amount hours that aworker node spends running one or more tasks (e.g., a search requests)for a customer. In another example, a DFS service can meter the amountof resources used to run one or more tasks rather than, or incombination with, the amount of time taken to complete the task(s). Insome embodiments, the licensing approaches include the total DFS hoursused per month billed on a per hour basis; the maximum capacity that canbe run at any one time, e.g. the total number of workers with a cap onthe amount of size of each worker defined by CPU and RAM available tothat worker; and finally a data volume based approach where the customeris charged by the amount of data brought into the DFS for processing.

FIG. 32 is a flow diagram illustrating monitoring and metering servicesof the DFS system according to some embodiments of the presentdisclosure. In the illustrated embodiment, in step 3202, the DFSservices can monitor one or more metrics of a DFS system. The DFSservices can monitor the DFS system for a variety of reasons. Forexample, in step 3204, a DFS service can track metrics and/or displaymonitored metrics or data indicative of the monitored metrics. Hence,the metrics can be preselected by, for example, a system operator oradministrator seeking to analyze system stabilities, instabilities, orvulnerabilities.

In some embodiments, the DFS services can meter use of the DFS system asa mechanism for billing customers. For example, in step 3206, the DFSservices can monitor specific metrics for specific customers that usethe DFS system. The metering services can differ depending on whetherthe customer has a subscription to use the DFS system or is using theDFS system on an on-demand basis. As such, a DFS service can run avalue-based licensing agreement that allows customers to have a fairexchange of value for their use of the DFS service.

In step 3208, a determination is made about whether a customer has asubscription to use the DFS system. The subscription can define thescope of a license granted to a customer to access or use the DFSsystem. The scope can define an amount of functionality available to thecustomer. The functionality can include, for example, the number ortypes of searches that can be performed on the DFS system. In someembodiments, the scope granted to a user can vary in proportion to cost.For example, customers can purchase subscriptions of different scope fordifferent prices, depending on the needs of the customers. As such, aDFS service can run a value-based licensing agreement that allowscustomers to have a fair exchange of value for their use of the DFSservice.

In step 3210, if the customer is subscribed, the DFS service can metermetrics based on a subscription purchased by the customer. For example,a subscription to a DFS service may limit the amount of searches that acustomer can submit to the DFS system. As such, the DFS service willmeter the number of searches that are submitted by the customer. Inanother example, a subscription to the DFS service may limit the time auser can actively access a DFS service. As such, the DFS service willmeter the amount of time that a user spends actively using the DFSservice.

In step 3212, a DFS service determines whether the customer's use of theDFS system exceeded a threshold amount granted by the subscription. Forexample, a customer may exceed the scope of a paid subscription by usingfunctionality not included in the paid subscription or using morefunctionality than that granted by the subscription. In someembodiments, the excess use can be measured with respect to a metricsuch as time or use of computing resources.

In step 3212, a DFS service determines whether a customer exceeded thescope of the customer's subscription. In step 3214, if the customer didnot exceed the subscription, no action is taken (e.g., the customer isnot charged additional fees). Referring back to step 3212, a variety ofactions can be taken if the customer has exceed the subscription. Instep 3216, the DFS service can charge the customer for the excess amountof the metered metric. For example, the DFS service may begin meteringthe amount of time a customer spends using the DFS system after athreshold amount of time has been exceeded. In step 3218, the DFSservice can alternatively or additionally prevent the customer fromaccessing the DFS system if the customer exceeds the subscription or hasnot paid the additional charges of step 3216.

Referring back to step 3208, if the customer is not subscribed to a DFSsubscription service, then customer may still access the DFS systemthrough a variety of other techniques. For example, a DFS service mayprovide limited or temporary access to the DFS system to anon-subscribed customer. In another example, a DFS service may provideaccess to the DFS service on-demand.

Either way, in step 3220, a DFS service meters metrics on anon-subscription basis. For example, in step 3222, the customer can payfor each instance the customer uses the DFS system. In another example,in step 3224, a DFS service can start charging a non-subscribed customerfor using the DFS system once the metrics of the service exceed athreshold amount. For example, a DFS service may provide free limitedaccess or temporary full access to the DFS system. When the measuringmetrics exceed the free limited access, the customer may be charged foraccess that exceeds the free amount. In either case, in step 3218, theDFS service can prevent the customer from accessing the DFS system ifthe measuring metrics exceed the threshold amount or the customer hasnot paid the charges of step 3222 or 3224. In some embodiments, a DFSserver can allow the customer to complete an active search that exceededa measuring metric but deny the customer from using the DFS system anyfurther until additional payment authorized.

11.0. Data Intake and Fabric System Architecture

FIG. 33 is a system diagram illustrating an environment 3300 foringesting and indexing data, and performing queries on one or moredatasets from one or more dataset sources. In the illustratedembodiment, the environment 3300 includes data sources 201, clientdevices 404, described in greater detail above with reference to FIG. 4, and external data sources 3318 communicatively coupled to a dataintake and query system 3301. The external data sources 3318 can besimilar to the external data systems 12-1, 12-2 described above withreference to FIG. 1A or the external data sources described above withreference to FIG. 4

In the illustrated embodiment, the data intake and query system 3301includes any combination of forwarders 204, indexers 206, data stores208, and a search head 210, as discussed in greater detail above withreference to FIGS. 2-4 . For example, the forwarders 204 can forwarddata from the data sources 203 to the indexers 206, the indexers 206 caningest, parse, index, and store the data in the data stores 208, and thesearch head 210 can receive queries from, and provide the results of thequeries to, client devices 404 on behalf of the system 3301.

In addition to forwarders 204, indexers 206, data stores 208, and thesearch head 210, the system 3301 further includes a search processmaster 3302 (in some embodiments also referred to as DFS master), one ormore query coordinators 3304 (in some embodiments also referred to assearch service providers), worker nodes 3306, and a query accelerationdata store 3308. In some embodiments, a workload advisor 3310, workloadcatalog 3312, node monitor 3314, and dataset compensation module 3316can be included in the search process master 3302. However, it will beunderstood that any one or any combination of the workload advisor 3310,workload catalog 3312, node monitor 3314, and dataset compensationmodule 3316 can be included elsewhere in the system 3301, such as in asa separate device or as part of a query coordinator 3304.

As will be described in greater detail below, the functionality of thesearch head 210 and the indexers 206 in the illustrated embodiment ofFIG. 33 can differ in some respects from the functionality describedpreviously with respect to other embodiments. For example, in theillustrated embodiment of FIG. 33 , the search head 210 can perform someprocessing on the query and then communicate the query to the searchprocess master 3302 and coordinator(s) 3304 for further processing andexecution. For example, the search head 210 can authenticate the clientdevice or user that sent the query, check the syntax and/or semantics ofthe query, or otherwise determine that the search request is valid. Insome cases, a daemon running on the search head 210 can receive a query.In response, the search head 210 can spawn a search process to furtherhandle the query, including communicating the query to the searchprocess master 3302 or query coordinator 3304. Upon completion of thequery, the search head 210 can receive the results of the query from thesearch process master 3302 or query coordinator 3304 and serve theresults to the client device 404. In such embodiments, the search head210 may not perform any additional processing on the results receivedfrom the search process master 3302 or query coordinator 3304. In somecases, upon receiving and communicating the results, the search head 210can terminate the search process.

In addition, the indexers 206 in the illustrated embodiment of FIG. 33can receive the relevant subqueries from the query coordinator 3304rather than the search head 210, search the corresponding data stores208 for relevant events, and provide their individual results of thesearch to the worker nodes 3306 instead of the search head 210 forfurther processing. As described previously, the indexers 206 cananalyze events for a query in parallel. For example, each indexer 206can search its corresponding data stores 208 in parallel and communicateits partial results to the worker nodes 3306.

The search head 210, search process master 3302, and query coordinator3304 can be implemented using separate computer systems, processors,isolated execution environments (e.g., container, virtual machines,etc.), or may alternatively comprise separate processes executing on oneor more computer systems, processors, or isolated executionenvironments. In some embodiments, running the search head 210, searchprocess master 3302, and/or query coordinator 3304 on the same machinecan increase performance of the system 3301 by reducing communicationsover networks. In either case, the search process master 3302 and querycoordinator 3304 can be communicatively coupled to the search head 210.

The search process master 3302 and query coordinator 3304 can be used toreduce the processing demands on the search head 210. Specifically, thesearch process master 3302 and coordinator 3304 can perform some of thepreliminary query processing to reduce the amount of processing done bythe search head 210 upon receipt of a query. In addition, the searchprocess master 3302 and coordinator 3304 can perform some of theprocessing on the results of the query to reduce the amount ofprocessing done by the search head 210 prior to communicating theresults to a client device. For example, upon receipt of a query, thesearch head 210 can determine that the query can be processed by thesearch process master 3302. In turn, the search process master 3302 canidentify a query coordinator 3304 that can process the query. In somecases, if there is not a query coordinator 3304 that can handle theincoming query, the search process master 3302 can spawn an additionalquery coordinator 3304 to handle the query.

The query coordinator(s) 3304 can coordinate the various tasks toexecute queries assigned to them and return the results to the searchhead 210. For example, as will be described in greater detail below, thequery coordinator 3304 can determine the amount of resources availablefor a query, allocate resources for the query, determine how the queryis to be broken up between dataset sources, generate commands for thedataset sources to execute, determine what tasks are to be handled bythe worker nodes 3306, spawn the worker nodes 3306 for the differenttasks, instruct different worker nodes 3306 to perform the differenttasks and where to route the results of each task, monitor the workernodes 3306 during the query, control the flow of data between the workernodes 3306, process the aggregate results from the worker nodes 3306,and send the finalized results to the search head 210 or to anotherdataset destination. In addition, the query coordinators 3304 canprovide data isolation across different searches based on role/accesscontrol, as well as fault tolerance (e.g., localized to a search head).For example, if a search operation fails, then its spawned querycoordinator 3304 may fail but other query coordinators 3304 for otherqueries can continue to operate. In addition, queries that are to beisolated from one another can use different query coordinators 3304.

The worker nodes 3306 can perform the various tasks assigned to them bya query coordinator 3304. For example, the worker nodes 3306 can intakedata from the various dataset sources, process the data according to thequery, collect results from the processing, combine results from variousoperations, route the results to various destinations, etc. In certaincases, the worker nodes 3306 and indexers 206 can be implemented usingseparate computer systems, processors, or isolated executionenvironments (e.g., containers, virtual machines, etc.), or mayalternatively comprise separate processes executing on one or morecomputer systems, processors, or virtual machines. Moreover, the workernodes 3306 can be similar to or perform functions similar to workernodes 214 described herein.

The query acceleration data store 3308 can be used to store datasets foraccelerated access. In some cases, the worker nodes 3306 can obtain datafrom the indexers 206, external data sources 3318, or other location(e.g., common storage, ingested data buffer, etc.) and store the data inthe query acceleration data store 3308. In such embodiments, when aquery is received that relates to the data stored in the queryacceleration data store 3308, the worker nodes 3306 can access the datain the query acceleration data store 3308 and process the data accordingto the query. Furthermore, if the query also includes a request fordatasets that are not in the query acceleration data store 3308, theworker nodes 3306 can begin working on the dataset obtained from thequery acceleration data store 3308, while also obtaining the otherdataset(s) from the other dataset source(s). In this way, a clientdevice 414 a-404 n can rapidly receive a response to a provided query,while the worker nodes 3306 obtain datasets from the other datasetsources.

The query acceleration data store 3308 can be, for example, adistributed in-memory database system, storage subsystem, and so on,which can maintain (e.g., store) datasets in both low-latency memory(e.g., random access memory, such as volatile or non-volatile memory)and longer-latency memory (e.g., solid state storage, disk drives, andso on). To increase efficiency and response times, the accelerated datastore 3308 can maintain particular datasets in the low-latency memory,and other datasets in the longer-latency memory. For example, thedatasets can be stored in-memory (non-limiting examples: RAM or volatilememory) with disk spillover (non-limiting examples: hard disks, diskdrive, non-volatile memory, etc.). In this way, the query accelerationdata store 3308 can be used to serve interactive or iterative searches.In some cases, datasets which are determined to be frequently accessedby a user can be stored in the lower-latency memory. Similarly, datasetsof less than a threshold size can be stored in the lower-latency memory.

As will be described below, a user can indicate in a query thatparticular datasets are to be stored in the query acceleration datastore 3308. The query can then indicate operations to be performed onthe particular datasets. For subsequent queries directed to theparticular datasets (e.g., queries that indicate other operations), theworker nodes 3306 can obtain information directly from the queryacceleration data store 3308. Additionally, since the query accelerationdata store 3308 can be utilized to service requests from differentclients 404 a-404 n, the query acceleration data store 3308 canimplement access controls (e.g., an access control list) with respect tothe stored datasets. In this way, the stored datasets can optionally beaccessible only to users associated with requests for the datasets.Optionally, a user who provides a query can indicate that one or moreother users are authorized to access particular requested datasets. Inthis way, the other users can utilize the stored datasets, thus reducinglatency associated with their queries.

In certain embodiments, the worker nodes 3306 can store data from anydataset source, including data from a dataset source that has not beentransformed by the nodes 3306, processed data (e.g., data that has beentransformed by the nodes 3306), partial results, or aggregated resultsfrom a query in the query acceleration data store 3308. In suchembodiments, the results stored in the query acceleration data store3308 can be served at a later time to the search head 210, combined withadditional results obtained from a later query, transformed or furtherprocessed by the worker nodes 3306, etc.

It will be understood that the system 3301 can include fewer or morecomponents as desired. For example, in some embodiments, the system 3301does not include a search head 210. In such embodiments, the searchprocess master 3302 can receive query requests from clients 404 andreturn results of the query to the client devices 404. Further, it willbe understood that in some embodiments, the functionality describedherein for one component can be performed by another component. Forexample, although the workload advisor 3310 and dataset compensationmodule 3316 are described as being implemented in the search processmaster 3302, it will be understood that these components and theirfunctionality can be implemented in the query coordinator 3304.Similarly, as will be described in greater detail below, in someembodiments, the nodes 3306 can be used to index data and store it inone or more data stores, such as the common storage or ingested databuffer, described in greater detail below.

11.1. Worker Nodes

FIG. 34 is a block diagram illustrating an embodiment of multiplemachines 3402, each having multiple nodes 3306-1, 3306-n (individuallyand collectively referred to as node 3306 or nodes 3306) residingthereon. The worker nodes 3306 across the various machines 3402 can becommunicatively coupled to each other, to the various components of thesystem 3301, such as the indexers 206, query coordinator 3304, searchhead 210, common storage, ingested data buffer, etc., and to theexternal data sources 3318.

The machines 3402 can be implemented using multi-core servers orcomputing systems and can include an operating system layer 3404 withwhich the nodes 3306 interact. For example, in some embodiments, eachmachine 3402 can include 32, 48, 64, or more processor cores, multipleterabytes of memory, etc.

In the illustrated embodiment, each node 3306 includes four processors3406, memory 3408, a monitoring module 3410, and aserialization/deserialization module 3412. It will be understood thateach node 3306 can include fewer or more components as desired.Furthermore, it will be understood that the nodes 3306 can includedifferent components and resources from each other. For example, node3306-1 can include fewer or more processors 3406 or memory 3408 than thenode 3306-n.

The processors 3406 and memory 3408 can be used by the nodes 3306 toperform the tasks assigned to it by the query coordinator 3304 and cancorrespond to a subset of the memory and processors of the machine 3402.Thus, reference to a worker node 3306 can also be understood to be areference to one or more processors 3406 of a worker node 3306 and viceversa (e.g., allocating, assigning, or selecting a worker node 3306 canrefer to allocating, assigning, or selecting one or more processors 3406of a worker node 3306). The serialization/deserialization module 3412can be used to serialize/deserialize data for communication betweencomponents of the system 3301, as will be described in greater detailbelow.

The monitoring module 3410 can be used to monitor the state andutilization rate of the node 3306 or processors 3406 and report theinformation to the search process master 3302 or query coordinator 3304.For example, the monitoring module 3410 can indicate the number ofprocessors in use by the node 3306, the utilization rate of eachprocessor, whether a processor is unavailable or not functioning, theamount of memory used by the processors 3406 or node 3306, etc.

In addition, each worker node 3306 can include one or more softwarecomponents or modules (“modules”) operable to carry out the functions ofthe system 3301 by communicating with the query coordinator 3304, theindexers 206, and the dataset sources. The modules can run on aprogramming interface of the worker nodes 3306. An example of such aninterface is APACHE SPARK, which is an open source computing frameworkthat can be used to execute the worker nodes 3306 with implicitparallelism and fault-tolerance.

In particular, SPARK includes an application programming interface (API)centered on a data structure called a resilient distributed dataset(RDD), which is a read-only multiset of data items distributed over acluster of machines (e.g., the devices running the worker nodes 3306).The RDDs function as a working set for distributed programs that offer aform of distributed shared memory.

Based on instructions received from the query coordinator 3304, theworker nodes 3306 can collect and process data or partial search resultsof a distributed network of data storage systems, and provide aggregatedpartial search results or finalized search results to the querycoordinator 3304 or other destination. Accordingly, the querycoordinator 3304 can act as a manager of the worker nodes 3306,including their distributed data storage systems, to extract, collect,and store partial search results via their modules running on acomputing framework such as SPARK. However, the embodiments disclosedherein are not limited to an implementation that uses SPARK. Instead,any open source or proprietary computing framework running on acomputing device that facilitates iterative, interactive, and/orexploratory data analysis coordinated with other computing devices canbe employed to run the modules 218 for the query coordinator 3304 toapply search queries to the distributed data systems.

As a non-limiting example, as part of processing a query, a node 3306can receive instructions from a query coordinator 3304 to perform one ormore tasks. For example, the node 3306 can be instructed to intake datafrom a particular dataset source, parse received data from a datasetsource to identify relevant data in the dataset, collect partial resultsfrom the parsing, join results from multiple datasets, or communicatepartial or completed results to a destination, etc. In some cases, theinstructions to perform a task can come in the form of a DAG. Inresponse, the node 3306 can determine what task it is to perform in theDAG, and execute it.

As part of performing the assigned task, the node 3306 can determine howmany processors 3406 to allocate to the different tasks. In someembodiments the node can determine that all processors 3406 are to beused for a particular task or only a subset of the processors 3406. Incertain embodiments, each processor 3406 of the node 3306 can be used inassociation with one or more a partitions to intake, process, or collectdata according to a task. Upon completion of the task, the node 3306 caninform the query coordinator 3304 that the task has been completed.

Depending on its context, partition can refer to different things. Forexample, in some cases, a partition can refer to a set of data in one ormore data stores, such as an index, or a stream of data. In certaincases, a partition can refer to smaller sets of data, such as when datais partitioned (or split up) into smaller parts. In yet other cases, oneor more partitions can be assigned to a processor 3406 or a worker node3306, and reference to a partition performing an action can refer to aprocessor 3406 performing the action on one or more groups of data ordata entries assigned thereto. Similarly, in some cases, reference toassigning a job or action to a partition can refer to the assignment ofa processor 3406 or worker node 3306 to perform that job or action. Forexample, the assignment of a partition to receive data from an externaldata source can refer to a processor 3406 receiving data from theexternal data source and grouping the data into one or more groups orpartitions of data. Thus, as used herein and based on the contextprovided, a partition can refer to an index, a task, a set or group ofdata, data entries, events, or records, or can refer to a processor 3406that performs a particular action on one or more groups or sets of data,data entries, or records. Further, in some instances, a partition canrefer to a group of data, data entries, events, or records andcomputer-executable instructions that indicate how the group of data isto be processed by a processor 3406 or worker node 3306.

When instructed to intake data, the processors 3406 of the node 3306 canbe used to communicate with a dataset source (non-limiting examples:external data sources 3318, indexers 206, common storage, queryacceleration data store 3308, ingested data buffer, etc.). Once the node3306 is in communication with the dataset source it can intake the datafrom the dataset source. As described in greater detail below, in someembodiments, multiple processors of a node (or different nodes) can beassigned to intake data from a particular source as one or morepartitions.

When instructed to parse or otherwise process data, the processors 3406of the node 3306 can be used to review the data and identify portions ofthe data that are relevant to the query. For example, if a queryincludes a request for events with certain errors or error types, theprocessors 3406 of the node 3306 can parse the incoming data to identifydifferent events, parse the different events to identify error fields orerror keywords in the events, and determine the error type of the error.In some cases, this processing can be similar to the processingdescribed in greater detail above with reference to the indexers 206processing data to identify relevant results in the data stores 208.

When instructed to collect data, the processors 3406 of the node 3306can be used to receive data from dataset sources or processing nodes.With continued reference to the error example, a collector partition orprocessor 3406 can collect all of the errors of a certain type from oneor more parsing partitions or processors 3406. For example, if there areseven possible types of errors coming from a particular dataset source,a collector partition could collect all type 1 errors (or events with atype 1 error), while another collector partition could collect all type2 errors (or events with a type 2 error), etc.

When instructed to join results from multiple datasets, the processors3406 of the node 3306 can be used to receive data corresponding to twodifferent datasets and combine or further process them. For example, ifdata is being retrieved from an external data source and a data store208 of the indexers 206, join partitions could be used to compare andcollate data from the different data stores in order to aggregate theresults.

When instructed to communicate results to a particular destination, theprocessors 3406 of the node 3306 can be used to prepare the data forcommunication to the destination and then communicate the data to thedestination. For example, in communicating the data to a particulardestination, the node 3306 can communicate with the particulardestination to ensure the data will be received. Once communication withthe destination has been established, the partition, or processorassociated with the partition, can begin sending the data to thedestination. As described in greater detail below, in some embodiments,multiple partitions of a node (or different nodes) can be assigned tocommunicate data to a particular destination. Furthermore, the nodes3306 can be instructed to transform the data so that the destination canproperly understand and store the data. Furthermore, the nodes cancommunicate the data to multiple destinations. For example, one copy ofthe data may be communicated to the query coordinator 3304 and anothercopy can be communicated to the query acceleration data store 3308.

The system 3301 is scalable to accommodate any number of worker nodes3306. As such, the system 3301 can scale to accommodate any number ofdistributed data systems upon which a search query can be applied andthe search results can be returned to the search head and presented in aconcise or comprehensive way for an analyst to obtain insights into bigdata that is greater in scope and provides deeper insights compared toexisting systems.

11.1.1. Serialization/Deserialization

In some cases, the serialization/deserialization module 3412 cangenerate and transmit serialized event groups. An event group caninclude the following information: number of events in the group, headerinformation, event information, and changes to the cache or cachedeltas. The serialization/deserialization module 3412 can identify thedifferences between the pieces of information using a type code ortoken. In certain cases, the type code can be in the form of a typebyte. For example, prior to identifying header information, theserialization/deserialization module 3412 can include a header type codeindicating that header information is to follow. Similarly, type codescan be used to identify event data or cache deltas.

The header information can indicate the number and order of fields inthe events, as well as the name of each field. Similarly, the eventinformation for each event can include the number of fields in theevent, as well as the value for that field. The cache deltas canidentify changes to make to the cache relied upon toserialize/deserialize the data.

As part of generating the group and serializing the data, theserialization/deserialization module 3412 can determine the number ofevents to group, determine the order and field names for the fields inthe events of the group, parse the events, determine the number offields for each event, identify and serialize serializable field valuesin the event fields, and identify cache deltas. In some cases, theserialization/deserialization module 3412 performs the various tasks ina single pass of the data, meaning that it performs the identification,parsing, and serializing during a single review of the data. In thismanner, the serialization/deserialization module 3412 can operate onstreaming data and avoid adding delay to theserialization/deserialization process.

4 (number of events) Header_Code 5 Data_Code “source” (number of fields)Data_Code “sourcetype” Data_Code “sale_type” Data_Code “company name”Data_Code “price” Cache_Delta_Code 5 “source” x15 (entries to add)“sourcetype” x16 “sale_type” x17 “company name” x18 “price” x19 0(entries to drop) Event_Code 5 Data_Code “ronnie.sv.splunk.com” (numberof fields in event) Data_Code “access_combined” Data_Code “SALE”Data_Code “World of Cheese” Data_Code “14.95” Cache_Delta_Code 5“ronnie.sv.splunk.com” x21 (number of new entries) “access_combined” x22“SALE” x23 “World of Cheese” x24 “14.95” x25 0 (entries to drop)Event_Code 5 Cache_Code x21 (number of fields in event) Cache_Code x22Data_Code “NO SALE” Cache_Code x24 Data_Code “16.75” Cache_Delta_Code 2“NO SALE” x26 (entries to add) “16.75” x27 0 (entries to drop)Event_Code 4 Cache_Code x21 (number of fields in event) Cache_Code x22Cache_Code x23 Cache_Code x24 Event_Code 5 Cache_Code x21 (number offields in event) Cache_Code x22 Cache_Code x23 Data_Code “World ofCheese” Data_Code “20.95” Cache_Delta_Code 2 “World of Cheese” (numberof new entries) “20.95” 1 x25 (entry to drop)

In some embodiments, an event group includes an identifier indicatingthe number of events in the group followed by a header type code and anumber of fields indicating the number of fields in the events. For eachfield designated by the header, the event group can include a type codeindicating whether the field name is already stored in cache or a typecode indicating that the field name is included. Depending on the typecode, the event group can include an identifier or the field name. Forexample, if the type code indicates the field name is stored in cache(e.g., a cache code), an identifier can be included to enable areceiving component to lookup the field name using the cache. If thetype code indicates the field name is not stored in cache (e.g., a datacode), the name of the field name can be included.

Similar to the header information, for each event in the event group,the event group can include number of fields in the event. For eachfield of the event, the event group can include a type code indicatingwhether the field name is already stored in cache or a type codeindicating that the field name is included.

As mentioned above, the event group can also include cache deltainformation. The cache delta information can include a cache delta typecode indicating that the cache is to be changed, a number of newentries, and a number of dropped entries. For each new entry the cachedelta information can include the data or string being cached, and anidentifier for the data. For each entry being dropped, the cache deltainformation can include the identifier of the cache entry to be dropped.

As a non-limiting example, consider the following portions of events:

ronnie.sv.splunk.com, access_combined, SALE, World of Cheese, 14.95

ronnie.sv.splunk.com, access_combined, NO SALE, World of Cheese, 16.75ronnie.sv.splunk.com, access_combined, SALE, World of Cheese

ronnie.sv.splunk.com, access_combined, SALE, Fondue Warrior, 20.95

In serializing the above-referenced events, theserialization/deserialization module 3412 can determine that the fieldnames for the events are source, sourcetype, sale_type, company name,and price and that this information is not in cache. Theserialization/deserialization module 3412 can then generate thefollowing event group:

By generating the group, the serialization/deserialization module 3412can reduce the amount of data communicated for each group. For example,instead of transmitting the string “ronnie.sv.splunk.com” each time, theserialization/deserialization module 3412 serializes it and thencommunicates the cache ID thereafter.

Entries can be added or dropped using a variety of techniques. In somecases, every new field value is cached. In certain cases, a field valueis cached after it has been identified a threshold number of times.Similarly, an entry can be dropped after a threshold number of events orevent groups have been processed without the particular value beingidentified. As a non-limiting example, the serialization/deserializationmodule 3412 can track X values at a time in a cache C and track up to Yvalues at a time that are not cached and how many time those values havebeen identified in a candidate set D. When a value is received, if it isin the cache C, then the identifier can be returned. If the value is notin the cache C, then it can be added to D. If Y has been reached in D,then the least recently used value can be dropped. If the count of thevalue in D satisfies a threshold T, then it can be moved to the cache Cand receive an identifier. If the size of C is more than X, then theleast recently used value in C can be dropped.

In some embodiments, the cache is built as the data is processed, andchanges are transmitted as they occur. For example, the receiver canstart with an empty cache, and apply each delta as it comes along. Asmentioned above, each delta can have two sections: new entries, anddropped entries. In certain embodiments, the receiver (or deserializer)does not drop cache entries until told to do so, otherwise, it may notbe able interpret identifiers received from the serializer. In suchembodiments, the serializer performs cache maintenance by informing thedeserializer when to drop entries. Upon receipt of such a command, thedeserializer can remove the identified entries.

11.2. Search Process Master

As mentioned above, the search process master 3302 can perform variousfunctions to reduce the workload of the search head 210. For example,the search process master 3302 can parse an incoming query and allocatethe query to a particular query coordinator 3304 for execution or spawnan additional query coordinator 3304 to execute the query. In addition,the search process master 3302 can track and store information regardingthe system 3301, queries, external data stores, etc., to aid the querycoordinator 3304 in processing and executing a particular query. In someembodiments, the search process master 3302.

In some cases, the search process master 3302 can determine whether aquery coordinator 3304 should be spawned based on user information. Forexample, for data protection or isolation, the search process master3302 can spawn query coordinators 3304 for different users. In addition,the search process master 3302 can spawn query coordinators 3304 if itdetermines that a query coordinator 3304 is over utilized.

In some cases, to accomplish these various tasks the search processmaster 3302 can include a workload advisor 3310, workload catalog 3312,node monitor 3314, and dataset compensation module 3316. Althoughillustrated as being a part of the search process master 3302, it willbe understood that any one or any combination of these components can beimplemented separately or included in one or more query coordinators3304. Furthermore, although illustrated as individual components, itwill be understood that any one or any combination of the workloadadvisor 3310, workload catalog 3312, node monitor 3314, and datasetcompensation module 3316 can be implemented by the same machine,processor, or computing device.

As a brief introduction, the workload advisor 3310 can be used toprovide resource allocation recommendations to a query coordinator 3304for processing queries, the workload catalog 3312 can store data relatedto previous queries, the node monitor 3314 can receive information fromthe worker nodes 3306 regarding a current status and/or utilization rateof the nodes 3306, and the dataset compensation module 3316 can be usedby the query coordinator 3304 to enhance interactions with external datasources.

11.2.1 Workload Catalog

The workload catalog 3312 can store relevant information to aid theworkload advisor 3310 in providing a resource allocation recommendationto a query coordinator 3304. As queries are received and processed bythe system 3301, the workload catalog 3312 can store relevantinformation about the queries to improve the workload advisor's 3310ability to recommend the appropriate amount of resources for each query.For example, the system 3301 can track any one or any combination of thefollowing data points about a query: which dataset sources wereaccessed, what was accessed in each dataset source (particular tables,buckets, etc.), the amount of data retrieved from the dataset sources(individually and collectively), the time taken to obtain the data fromthe dataset sources, the number of nodes 3306 used to obtain the datafrom each dataset source, the utilization rate of the nodes 3306 whileobtaining the data from the dataset source, the number oftransformations or phases (processing, collecting, reducing, joining,branching, etc.) performed on the data obtained from the datasetsources, the time to complete each transformation, the number of nodes3306 assigned to each phase, the utilization rate of each node 3306assigned to the particular phase, the processing performed by the querycoordinator 3304 on results (individual or aggregatee), time to store ordeliver results to a particular destination, resources used tostore/deliver results, total time to complete query, time of day ofquery request, etc. Furthermore, the workload catalog can includeidentifying information corresponding to the datasets with which thesystem interacts (e.g., indexers, common storage, ingested data buffer,external data sources, query acceleration data store, etc.). Thisinformation can include, but is not limited to, relationships betweendatasets, size of dataset, rate of growth of dataset, type of data,selectivity of dataset, provider of dataset, indicator for privateinformation (e.g., personal health information, etc.), trustworthinessof a dataset, dataset preferences, etc.

The workload catalog 3312 can collect the data from the variouscomponents of the system 3301, such as the query coordinator 3304,worker nodes 3306, indexers 206, etc. For example, for each taskperformed by each node 3306, the node 3306 can report relevant timingand resource utilization information to the query coordinator 3304 ordirectly to the workload catalog 3312. Similarly, the query coordinator3304 can report relevant timing, usage, and data information for eachphase of a search, each transformation of data, or for a total query.

Using the information collected in the workload catalog 3312, theworkload advisor 3310 can estimate the compute cost to perform aparticular data transformation or query, or to access a particulardataset. Further, the workload advisor can determine the amount ofresources (nodes, memory, processors, partitions, etc.) to recommend fora query in order to provide the results within a particular amount oftime.

11.2.2 Node Monitor

The node monitor 3314 can also store relevant information to aid theworkload advisor 3310 in providing a resource allocation recommendation.For example, the node monitor 3314 can track and store informationregarding any one or any combination of: total number of processors ornodes in the system 3301, number of processors or nodes that are notavailable or not functioning, number of available processors or nodes,utilization rate of the processors or nodes, number of worker nodes,current tasks being completed by the worker nodes 3306 or processors,estimated time to complete a task by the nodes 3306 or processors,amount of available memory, total memory in the system 3301, tasksawaiting execution by the nodes 3306 or processors, etc.

The node monitor 3314 can collect the relevant information bycommunicating with the monitoring module 3410 of each node 3306 of thesystem 3301. As described above, the monitoring modules 3410 of eachnode 3306 can report relevant information about the node state andutilization rate. Using the information from the node monitor 3314, theworkload advisor 3310 can ascertain the general state of any particularprocessor, node, or the system 3301, and determine the number of nodes3306 or processors 3406 available for a particular task or query.

11.2.3 Dataset Compensation

As discussed above, the external data sources 3318 with which the system3301 can interact vary significantly. For example, some external datasource may have processing capabilities that can be used to perform someprocessing on the data that resides there prior to communicating thedata to the nodes 3306. In addition, the external data sources 3318 maysupport parallel reads from multiple partitions. Conversely, otherexternal data sources 3318 may not be able to perform much, if any,processing on the data contained therein and/or may only be able toprovide serial reads from a single partition. Additionally, eachexternal data source 3318 may have particular requirements forinteracting with it, such as a particular API, throttling requirements,etc. Further, the type and amount of data stored in each external datasource 3318 can vary significantly. As such, the system's 3301interaction with the different external data sources 3318 can varysignificantly.

To aid the system 3301 in interacting with the different external datasources 3318, the dataset compensation model 3316 can include relevantinformation related to each external data source 3318 with which thesystem 3301 can interact. For example, the dataset compensation model3316 can include any one or any combination of: the amount of datastored in an external data source 3318, the type of data stored in anexternal data source, query commands supported by an external datasource (e.g., aggregation, filtering ordering), query translator totranslate a query into tasks supported by an external data source, thefile system type and hierarchy of the external data source 3318, numberof partitions supported by an external data source 3318, endpointlocations (e.g., location of processing nodes or processors), throttlingrequirements (e.g., number and rate at which requests can be sent to theexternal data source), etc.

The information about each external data source 3318 can be collected ina variety of ways. In some cases, some of the information about theexternal data source 3318 can be received when a customer sets up theexternal data source 3318 for use with the system 3301. For example, acustomer can indicate the type of external data source 3318 e.g., MySQL,PostgreSQL, and Oracle databases; NoSQL data stores like Cassandra,Mongo DB, cloud storage like Amazon S3 HDFS, etc. Based on thisinformation, the system 3301 can determine certain characteristics aboutthe external data store 3318, such as whether it supports multiplepartitions.

In addition, as discussed herein, different dataset sources havedifferent capabilities. For example, not only can different datasetssources support a different number of partitions, but the datasetsources can support different functions. For example, some datasetsources may be capable of data aggregation, filtering, or ordering,etc., while others may not be. The dataset compensation module 3316 canstore the capabilities of the different dataset sources to aid inproviding a seamless experience to users.

In certain cases, the system 3301 can collect relevant information aboutan external data source by communicating with it. For example, the querycoordinator 3304 or a worker node 3306 can interact with the externaldata source 3318 to determine the number of partitions available foraccessing data. In some cases, the number of available partitions maychange as computing resources on the external data source 3318 becomeavailable or unavailable, etc. In addition, when the system 3301accesses the external data source 3318 as part of a query it can trackrelevant information, such as the tables or amount of data accessed,tasks that the external data source was able to perform, etc. Similarly,the system 3301 can interact with an external data source 3318 toidentify the endpoint that will handle any subqueries and its location.The endpoint and endpoint location may change depending on the subquerythat is to be run on the external data source. Accordingly, in someembodiments, the system 3301 can request endpoint information with eachquery that is to access the particular external data source.

Using the information about the external data sources 3318, a querycoordinator 3304 can determine how to interact with it and how toprocess data obtained from the external data source 3318. For example,if an external data source 3318 supports parallel reads, the querycoordinator 3304 can allocate multiple worker nodes 3306 to read thedata from the external data source 3318 in parallel. In someembodiments, the query coordinator 3304 can allocate sufficient workernodes 3306 or processors 3406 to establish a 1:1 relationship with theavailable partitions at the external data source 3318. Similarly, if theexternal data source 3318 can perform some processing of the data, thequery coordinator 3304 can use the information from the datasetcompensation module 3316 to translate the query into commands understoodby the external data source 3318 and push some processing to theexternal data source 3318, thereby reducing the amount of system 3301resources (e.g., nodes 3306) used to process the query.

Furthermore, in some cases, using the dataset compensation module 3316,the query coordinator can determine the amount of data in the differentexternal data sources that will be accessed by a particular query. Usingthat information, the query coordinator 3304 can intelligently interactwith the external data sources 3318. For example, if the querycoordinator 3304 determines that data with similar characteristics intwo external data sources are to be accessed and the data from each willeventually be combined, the query coordinator 3304 can first interactwith or query the external data source 3318 that includes less data andthen using information gleaned from that data prepare a more narrowlytailored query for the external data source 3318 with more data.

As a specific example, suppose a user wants to identify the source of aparticular error using information from an HDFS data source and anOracle data source, but does not know what the error is or whatgenerated it. To do so, the user enters a query that includes a requestto identify errors generated within a particular timeframe and stored inan HDFS data source and an Oracle data source and then correlate theerrors based on the error source. Based on the query, the querycoordinator 3304 determines that a union operation is to be performed onthe data from the HDFS data source and the Oracle data source based onthe source of the errors.

Additionally, suppose that the dataset compensation module 3316 hasidentified the HDFS data source as being relatively small and identifiedthe Oracle data source as being significantly larger than the HDFS datasource. Accordingly, based on the information in the datasetcompensation module 3316, the query coordinator 3304 can instruct thenodes 3306 to first intake and process the data from the HDFS datasource. Suppose that by doing so, the nodes 3306 determine that the HDFSdata source only includes fifty types of errors in the specifiedtimeframe from ten sources. Accordingly, using that information, thequery coordinator 3304 can instruct the nodes 3306 to limit the intakeof data from the Oracle data store based on the error type and/or thesource based on the error types and sources identified by firstanalyzing the HDFS data source.

As such, the query coordinator 3304 can reduce the amount of datarequested by the Oracle data store and the amount of processing neededto obtain the relevant result. For example, if the Oracle data storeincluded two hundred error types from one hundred sources, the querycoordinator 3304 avoided having to intake and process the data from allone hundred sources. Instead only the data from sources that matched theten sources from the HDFS data source were requested and processed bythe nodes 3306.

11.3. Query Coordinator

The query coordinator(s) 3304 can act as the primary coordinator orcontroller for queries that are assigned to it by the search head 210 orsearch process master 3302. As such, the query coordinator can process aquery, identify the resources to be used to execute the query, controland monitor the nodes to execute the query, process aggregate results ofthe query, and provide finalized results to the search head 210 orsearch process master 3302 for delivery to a client device 404.

11.3.1. Query Processing

Upon receipt of a query, the query coordinator 3304 can analyze thequery. In some cases analyzing the query can include verifying that thequery is semantically correct or performing other checks on the query todetermine whether it is executable by the system. In addition, the querycoordinator 3304 can analyze the query to identify the dataset sourcesthat are to be accessed and to define an executable search process. Forexample, the query coordinator 3304 can determine whether data from theindexers 206, external data sources 3318, query acceleration data store3308, or other dataset sources (e.g., common storage, ingested databuffers, etc.) are to be accessed to obtain the relevant datasets.

As part of defining the executable search process, the query coordinator3304 can identify the different entities that can perform someprocessing on the datasets. For example, the query coordinator 3304 candetermine what portion(s) of the query can be delegated to the indexers206, nodes 3306, and external data sources 3318, and what portions ofthe query can be executed by the query coordinator 3304, search processmaster 3302, or search head 210. For tasks that can be completed by theindexers 206, the query coordinator 3304 can generate task instructionsfor the indexers 206 to complete, as well as instructions to route allresults from the indexers 206 to the nodes 3306. For tasks that can becompleted by the external data sources 3318, the query coordinator 3304can use the dataset compensation module 3316 to generate taskinstructions for the external data sources 3318 and to determine how toset up the nodes 3306 to receive data from the external data sources3318.

In addition, as part of defining the executable search process, thequery coordinator 3304 can generate a logical directed acyclic graph(DAG) based on the query. FIG. 35 is a diagram illustrating anembodiment of a DAG 2000 generated as part of a search process. In theillustrated embodiment, the DAG 2000 includes seven vertices and sixedges, with each edge directed from one vertex to another, such that bystarting at any particular vertex and following a consistently-directedsequence of edges the DAG 2000 will not return to the same vertex.

Here, the DAG 2000 can correspond to a topological ordering of searchphases, or layers, performed by the nodes 3306. As such, a sequence ofthe vertices can represent a sequence of search phases such that eachedge is directed from earlier to later in the sequence of search phases.For example, the DAG 2000 may be defined based on a search string foreach phase or metadata associated with a search string. The metadata maybe indicative of an ordering of the search phases such as, for example,whether results of any search string depend on results of another searchstring such that the later search string must follow the former searchstring sequentially in the DAG 2000.

In the illustrated embodiment of FIG. 35 , the DAG 2000 can correspondto a query that identifies data from two dataset sources that are to becombined and then communicated to different locations. Accordingly, theDAG 2000 includes intake vertices 3502, 3508, a process vertex 3504,collect vertices 3506, 3510, a join vertex 3512, and a branch vertex3514.

Each vertex 3502, 3504, 3506, 3508, 3510, 3512, 3514 can correspond to asearch phase performed by one or more processors 3406 of one or morenodes 3306 on a particular set of data or partitions. For example, theintake, process, and collect vertices 3502, 3504, 3506 can correspond todata search phases, or transformations, on data received from a firstdataset source. More specifically, the intake phase or vertex 3502 cancorrespond to the processing of one or more partitions associated withdata received from the first dataset source, the process phase 3504 cancorrespond to the processing of one or more partitions that resultedfrom the intake phase 3502, and the collect phase 3506 can correspond toone or more partitions that collect the results of the processing of thepartitions in the process phase 3504.

Similarly, the intake and collect vertices 3508, 3510 can correspond todata search phases performed using one or more partitions or by one ormore processors 3406 on data received from a second dataset source. Forexample, the intake phase 3508 can correspond to one or more partitionsthat receive data from the second dataset source and the collect phase3510 can correspond to one or more partitions that collect the resultsfrom the partitions in the intake phase 3508.

The join and branch phases 3512, 3514 can correspond to data searchphases performed by one or more processors 3406 on partitionscorresponding to data received from the different branches of the DAG2000. For example, the join phase 3512 can correspond to one or morepartitions used to combine the data received from the partitions in thecollect phases 3506, 3510. The branch phase 3514 can correspond to oneor more partitions used to communicate results of the join phase 3512 toone or more destinations. For example, the partitions in the branchphase 3514, or processors assigned to the partitions in the branch phase3514, can communicate results of the query to the query coordinator3304, an external data source 3318, accelerated data source 3308,ingested data buffer, etc.

It will be understood that the number, order, and types of search phasesin the DAG 2000 can be determined based on the query. As a non-limitingexample, consider a query that indicates data is to be obtained fromcommon storage and an Oracle database, collated, and the results sent tothe query coordinator 3304 and an HDFS data store. In this example, inresponse to determining that the common storage do not provideprocessing capabilities, the query coordinator 3304 can generatevertices 3502, 3504, 3506 indicating that an intake phase 3502, processphase 3504, and collect phase 3506 will be used to process the data fromthe common storage sufficiently to be combined with data from the Oracledatabase. Similarly, based on a determination that the Oracle databasecan perform some processing capabilities, the query coordinator cangenerate vertices 3508, 3510 indicating that an intake phase 3508 andcollect phase 3510 will be used to sufficiently process the data fromthe Oracle database for combination with the data from the commonstorage.

The query coordinator 3304 can further generate the join phase 3512based on the query indicating that the data from the Oracle database andcommon storage is to be collated or otherwise combined (e.g., joined,unioned, etc.). In addition, based on the query indicating that theresults of the combination are to be communicated to the querycoordinator 3304 and the HDFS data store, the query coordinator 3304 cangenerate the branch phase 3514. As mentioned above, in each phase, thequery coordinator 3304 can allocate one or more nodes 3306 or processors3406 to perform the particular search phase on the partitions of theparticular phase.

It will be understood that the DAG 2000 is a non-limiting example of thesearch phases that can be included as part of a search process. In somecases, depending on the query, the DAG 2000 can include fewer or morephases of any type. For example, the DAG 2000 can include fewer or moreintake phases depending on the number of dataset sources. Additionally,depending on the particular processing requirements of a query, the DAG2000 can include multiple processing, collect, join, union, stats, orbranch phases, in any order.

In addition to determining the number and types of search phases for asearch process, the query coordinator 3304 can calculate the relativecost of each phase of the search process, determine the amount ofresources to allocate for each phase of the search process, generatetasks and instructions for particular nodes to be assigned to aparticular search process, generate instructions for dataset sources,generate tasks for itself and/or the search head 210, etc.

To calculate the relative cost of each phase of the search process anddetermine the amount of resources to allocate for each phase of thesearch process, the query coordinator 3304 can communicate with theworkload advisor 3310, workload catalog 3312, and/or the node monitor3314. As described previously, the workload advisor 3310 can use thedata collected in the workload catalog 3312 to determine the cost of aquery or an individual transformation or search phase of a searchprocess and to provide a resource allocation recommendation.Furthermore, the workload advisor 3310 can use the data from the nodemonitor module 3314 to determine the available resources in the system3301. Using this information, the query coordinator 3304 can determinethe cost for each search phase, the amount of resources available forallocation, and the amount of resources to allocate for each searchphase.

In determining the amount of resources to allocate for each searchphase, the query coordinator 3304 can also generate the tasks andinstructions for each node 3306. The instructions can include computerexecutable instructions that when executed by the node 3306 cause thenode 3306 to perform the task assigned to it by the query coordinator3304. For example, for nodes 3306 that are to be assigned to an intakephase 3502, 3508, the query coordinator 3304 can generate instructionson how to access a particular dataset source, what instructions are tobe sent to the dataset source, what to do with the data received fromthe dataset source, where do send the received data, how to perform anyload balancing or other tasks assigned to it, etc. For nodes 3306 thatare to process data in the process phase 3504, the query coordinator3304 can generate instructions indicating how to parse the receiveddata, relevant fields or keywords that are to be identified in the data,what to do with the identified field and keywords, where to send theresults of the processing, etc. Similarly, for nodes 3306 in the collectphases 3506, 3510, join phase 3512, or branch phase 3514, the querycoordinator 3304 can generate task instructions so that the nodes 3306(which can also refer to one or more processors 3406 within a workernode 3306, execution environments within a worker node 3306 or processor3406 of a worker node 3306, such as a virtualized computing device orsoftware-based container, etc.) are able to perform the task assigned tothat particular phase or partition. The task instructions can tell thenodes 3306 what data or partitions they are to process, how they are toprocess the data, where they are to route the results of the processingof that phase, either between each other or to another destination. Insome cases, the query coordinator 3304 can generate the tasks andinstructions for all nodes 3306 and send the instructions to all of theallocated nodes 3306. Between them, the nodes 3306 can determine orassign which nodes 3306 will execute the different instructions andtasks. The instructions sent to the nodes 3306 or processors 3406 caninclude additional parameters, such as a preference to use nodes 3306 orprocessors 3406 on the same machine 3402 for subsequent tasks. Suchinstructions can help reduce the amount of data communicated over thenetwork, etc. Each node 3306 can assign specific processors 3406 and/ormemory 3408 to execute particular tasks or partitions.

In some embodiments, to generate instructions for the dataset sources,the query coordinator 3304 can use the dataset compensation module 3316.As described previously, the dataset compensation module 3316 caninclude relevant data about external data sources including, inter alia,processing abilities of the external dataset sources, number ofpartitions of the external dataset sources, instruction translators,etc. Using this information, the query coordinator 3304 can determinewhat processing to assign to the external data sources, and generateinstructions that will be understood by the external data sources. Inaddition, the query coordinator 3304 can have access to similarinformation about other dataset sources and/or communicate with thedataset sources to determine their processing capabilities and how tointeract with them (non-limiting examples: number of partitions to use,processing that can be pushed to the dataset source, etc.). Similarly,the query coordinator 3304 can determine how to interact with thedataset destinations so that the datasets can be properly sent to thecorrect location in a manner that the destination can store themcorrectly.

In some cases, the query coordinator 3304 can interact with onepartition of the external dataset source using multiple nodes 3306 orprocessors 3406. For example, the query coordinator 3304 can allocatemultiple nodes 3306 or processors 3406 to interact with a singlepartition of the external dataset source. The query coordinator 3304 canbreak up a query or a subquery into multiple parts. Each part can beassigned to a different node 3306 or processor 3406, which cancommunicate the subqueries to the external dataset source. Thus,unbeknownst to the external dataset source, it can concurrently processdata from a single query.

Furthermore, the query coordinator 3304 can determine the order forconducting the search process. As mentioned above, in some embodiments,the query coordinator 3304 can determine that processing datafrom onedataset source could speed up the search process as a whole(non-limiting example: using data from one dataset source to generate amore targeted search of another dataset source). Accordingly, the querycoordinator 3304 can determine that one or more search phases are to becompleted first and then based on information obtained from the searchphase, additional search phases are to be initiated. Similarly, otheroptimizations can be determined by the query coordinator 3304. Suchoptimizations can include, but are not limited to, pushing processing tothe edges (e.g., to external data sources, etc.), identifying fields ina query that are key to the query and reducing processing based on theidentified field (e.g., if a relevant field is identified in a finalprocessing step, use the field to narrow the set of data that issearched for earlier in the search process), allocating the query tonodes that are physically close to each other or on the same machine,etc.

11.3.2. Query Execution and Node Control

Once the query is processed and the search scheme determined, the querycoordinator 3304 can initiate the query execution. In some cases, ininitiating the query, the query coordinator 3304 can communicate thegenerated task instructions to the various locations that will processthe data. For example, the query coordinator 3304 can communicate taskinstructions to the indexers 206, based on a determination that theindexers 206 are to perform some amount of processing on the dataset.Similarly, the query coordinator 3304 can communicate task instructionsto the nodes 3306, external data sources 3318, query acceleration datastore 3308, common storage, and/or ingested data buffer, etc.

In some embodiments, rather than communicating with the various datasetsources, the query coordinator 3304 can generate task instructions forthe nodes 3306 to interact with the dataset sources such that thedataset sources receive any task instructions from the nodes 3306 asopposed to the query coordinator 3304. For example, rather thancommunicating the task instructions directly to a dataset source, thequery coordinator 3304 can assign one or more nodes 3306 to communicatetask instructions to the external data sources 3318, indexers 206, orquery acceleration data store 3308. In certain embodiments, the querycoordinator 3304 can communicate the same search scheme or taskinstructions to the nodes 3306 or processors 3406 of the nodes 3306 thathave been allocated for the query. The allocated nodes 3306 orprocessors 3406 of the nodes 3306 can then assign different nodes 3306to perform different portions of the search scheme.

Upon receipt of the task instructions, the dataset sources and nodes3306 can begin operating in parallel. For example, if task instructionsare sent to the indexers 206 and to the nodes 3306, both can beginexecuting the instructions in parallel. In executing the taskinstructions, the nodes 3306 can organize their processors 3406according to task instructions. For example, some of the nodes 3306 canallocate one or more processors 3406 as part of an intake phase, anotherprocessor 3406 as part of a processing phase, etc. In some cases, allprocessors 3406 of a node 3306 can be allocated to the same task or todifferent tasks. For example, during an intake phase, some or allprocessors 3406 of a node 3306 can be allocated to tasks of the intakephase, and during a processing phase, all processors 3406 of a node 3306can be allocated to tasks of the processing phase, etc. In certainembodiments, it can be beneficial to allocate processors 3406 from thesame node 3306 to different tasks or subsequent phases to reduce networktraffic between nodes 3306 or machines 3402.

FIG. 36 is a block diagram illustrating an embodiment of layers ofpartitions used to implement various search phases of a query. In somecases, the layers can correspond to search phases in a DAG, such as theDAG 2000 described in greater detail above. In the illustratedembodiment of FIG. 36 , based on task instructions received from thequery coordinator 3304, various partitions are used to perform differentsearch phases on data coming from a dataset source 3602. As describedpreviously, the dataset source 3602 can correspond to indexers 206,external data sources 3318, the query acceleration data store 3308,common storage, an ingested data buffer, or other source of data fromwhich the nodes 3306 can receive data.

The processors 3406 or worker nodes 3306 assigned to each layer caninteract with the data or partitions based on task instructions receivedby the query coordinator 3304. In the illustrated embodiment of FIG. 36, the partitions in the intake layer 3604 can correspond to datareceived from the dataset source 3602, which can be communicated ortransformed to partitions in the processing layer 3606 by worker nodes3306 in a load-balanced fashion. The worker nodes 3306 can process thedata of the partitions in the processing layer 3606 based on the taskinstructions, which are generated based on the query, and provide ortransform the results to or into the partitions in the collector layer3608. Similarly, upon completing their assigned task, the processors3406 of the worker nodes 3306 associated with the partitions in thecollector layer 3608 can communicate the results of their processing tothe branch layer 3610. In the illustrated embodiment of FIG. 36 , thebranch layer 3610 communicates the results received from the partitionsin the collector layer 3608 to a first dataset destination 3614 and topartitions in a storage layer 3612 for storage in a second datasetdestination 3616. It will be understood that fewer or more layers can beincluded as desired, and can be based on the content of the particularquery being executed. Furthermore, it will be understood that the layerscan correspond to different map-reduce procedures or commands. Forexample, as described herein, in the illustrated embodiments, theprocessing layer 3606 can correspond to a map procedure and thecollector layer 3608 can correspond to a reduce procedure. However, asdescribed herein, it will be understood that various layers cancorrespond to map or reduce procedures.

In the illustrated embodiment, four partitions are included in theintake layer 3604, eight partitions are included in the processing layer3606, five partitions are included in the collector layer 3608, onepartition is included in the branch layer 3610, and three partitions areincluded in the storage layer 3612. In some embodiments, the number ofpartitions can correspond to the number of tasks or amount of data beingprocessed in the layer. Thus, there is a larger amount of data to beprocessed in the processing layer 3606 than in the intake layer 3604 orcollector layer 3608. Further, it will be understood that fewer or morepartitions can be used in any layer as desired and fewer or additionallayers can be included. For example, based on a query that indicatesmultiple dataset sources are to be accessed, the query coordinator 3304can allocate separate intake, processing, and collector layers 3604,3606, 3608 for each dataset source 3602. Furthermore, based on the querycommands, the query coordinator can allocate additional layers, such asa join layer to combine data received from multiple dataset sources,etc.

In determining the number of partitions and/or processor 3406 for eachsearch phase or layer, the query coordinator 3304 can use the workloadadvisor 3310 and/or dataset compensation module 3316. For example, theworkload advisor 3310 can use historical data about executing individualsearch phases in queries to recommend an allocation scheme that providessufficient resources to process the query in a reasonable amount oftime. Furthermore, in some embodiments, the query coordinator 3304 candetermine the number of partitions based on the amount of processors3406 assigned to the query, the amount of memory available, the amountof data (or number of events) to be processed, and information about theevents or query, such as the number of fields used in the query or partof the events.

In some cases, the query coordinator 3304 can allocate partitions orprocessors 3406 for the intake layer 3604 and storage layer 3612 basedon information about the number of partitions available for reading fromthe dataset source 3602 and writing data to the dataset destination3616, respectively. The query coordinator 3304 can obtain theinformation about the dataset source 3602 or dataset destination 3616from a number of locations, including, but not limited to, the workloadcatalog 3312, the dataset compensation module 3316, or from the datasetsource 3602 or dataset destination 3616 itself. The information caninform the query coordinator 3304 as to the number of partitionsavailable for reading from the dataset source 3602 and writing to thedataset destination 3616.

In some cases, the query coordinator 3304 can allocate worker nodes 3306or processors 3406 in the intake layer 3604 or the storage layer 3612 tohave a one-to-one, one-to-many, or many-to-one correspondence withpartitions supported by the dataset source 3602 or dataset destination3616, respectively. The correspondence between the worker nodes 3306 orprocessors 3406 in the intake or storage layer 3604, 3612 and thepartitions supported by the dataset source or destination 3602, 3616,respectively, can be based on a threshold number of partitions, the typeof the dataset source/destination, etc.

In certain embodiments, if the query coordinator 3304 determines thatthe dataset source 3602 (or dataset destination 3616) has or supports anumber of partitions that satisfies a threshold number of partitions ordetermines that the number of partitions supported by the dataset source3602 (or dataset destination 3616) can be matched without overextendingthe nodes 3306, the query coordinator 3304 can allocate nodes 3306 orprocessors 3406 in the intake layer 3604 (or storage layer 3612) to havea one-to-one correspondence to partitions supported by the datasetsource 3602 (or dataset destination 3616).

The number of partitions that satisfy the threshold number of partitionscan be determined based on the number of nodes 3306 or processors 3406in the system 3301, the number of available nodes 3306 in the system3301, scheduled usage of nodes 3306, amount of memory available, etc.Accordingly, the threshold number of partitions can be dynamic dependingon the status of the processors 3406, nodes 3306, or the system 3301.For example, if a large number of nodes 3306 are available, thethreshold number of nodes can be larger, whereas, if only arelativelysmall number of nodes 3306 are available, the threshold number can besmaller. Similarly, if the workload advisor 33010 expects a large numberof queries in the near term it can allocate fewer worker nodes 3306 orprocessors 3406 to an individual query. Alternatively, if the workloadadvisor 33010 does not expect many queries in the near term it canallocate a greater number of worker nodes 3306 or processors 3406 to anindividual query.

In some cases, the query coordinator 3304 can determine whether to matchthe number of partitions supported by the dataset source 3602 or datasetdestination 3616 with corresponding worker nodes 3306 or processors 3406in the intake layer 3604 or storage layer 3612, respectively, based onthe type of the dataset source 3602 or dataset destination 3616. Forexample, the query coordinator 3304 can determine there should be aone-to-one correspondence of intake layer 3604 worker nodes 3306 orprocessors 3406 to dataset source 3602 supported partitions (or storagelayer 3612 worker nodes 3306 or processors 3406 to dataset destination3616 supported partitions) when the dataset source 3602 (or datasetdestination 3616) is an external data source or ingested data buffer andthat there should be a one-to-multiple correspondence when the datasetsource 3602 (or dataset destination 3616) is indexers 206, commonstorage, query acceleration data store 3308, etc.

As a non-limiting example, if the dataset source 3602 is an externaldata source or ingested data buffer that supports four partitions andthe query coordinator 3304 determines that it can support a one-to-onecorrespondence, the query coordinator 3304 can allocate four workernodes 3306 or processors 3406 to the intake layer 3604. The allocatedworker nodes 3306 or processors can intake the data as four or morepartitions, as illustrated in FIG. 36 . Similarly, if the datasetdestination 3616 is an external data source or ingested data buffer thatsupports three partitions and the query coordinator 3304 determines thatit can support a one-to-one correspondence, the query coordinator 3304can allocate three worker nodes 3306 or processors 3406 to the storagelayer 3612, which can result in three or more partitions being worked onconcurrently, as illustrated in FIG. 36 .

As another non-limiting example, if the dataset source 3602 (or datasetdestination 3616) is indexers 206, common storage, or query accelerationdata stores 3308 that supports hundreds of potential partitions, and/orthe query coordinator 3304 determines that it cannot support aone-to-one correspondence, it can allocate four worker nodes 3306 orprocessors 3406 to the intake layer 3604 resulting in at least fourpartitions being worked on concurrently (or three worker nodes 3306 orprocessors 3406 to the storage layer 3612 resulting in at least threepartitions being worked on concurrently), as illustrated in FIG. 36 .However, it will be understood that in some embodiments, the querycoordinator 3304 can allocate all worker nodes 3306 or all worker nodes3306 assigned to its query to the intake layer 3604 for reading datafrom dataset source 3602 or sending data to dataset destination 3616.

In addition, during intake of the data from the dataset source 3602, thequery coordinator 3304 can dynamically adjust the number of worker nodes3306 or processors 3406 in the intake layer 3604. For example, if anadditional partition of the dataset source 3602 becomes available or oneof the partitions becomes unavailable, the query coordinator 3304 candynamically increase or decrease the number of worker nodes 3306 orprocessors 3406 in the intake layer 3604. Similarly, if the querycoordinator 3304 determines that the intake layer 3604 is taking toomuch time and additional resources are available, it can dynamicallyincrease the number of worker nodes 3306 or processors 3406 in theintake layer 3604. In addition, if the query coordinator 3304 determinesthat additional resources are available or become unavailable, it candynamically increase or decrease the number of worker nodes 3306 orprocessors 3406 in the intake layer 3604. Similarly, the querycoordinator can dynamically adjust the number of worker nodes 3306 orprocessors 3406 in the storage layer 3612.

Similar to the intake layer 3604 and storage layer 3612, the querycoordinator 3304 can estimate or determine a number of partitions forthe different search layers 3606, 3608, 3610 based on information aboutthe query and information in the workload catalog 3312 and allocateworker nodes 3306 or processors 3406 accordingly. For example, the querymay include requests to process the data in a way that is resourceintensive, resulting in a larger number of partitions. As such, thequery coordinator 3304 can estimate that a larger number of partitionswill be used in the processing layer and allocate additional workernodes 3306 or processors 3406 to the processing layer 3606 or usemultiple processing layers 3606 to process the data. In some cases, morepartitions, worker nodes 3306, and/or processors 3406 can be allocatedto the search layers for queries of larger datasets.

In addition, during execution of the query, the query coordinator 3304can monitor the partitions or processors 3406 in the search layers 3606,3608, 3610 and dynamically adjust the number of partitions or processors3406 in each depending on the status of the individual partitions, thestatus of the nodes 3306, the status of the query, etc. For example, ifa partition becomes larger than a threshold size due to high cardinalityor other reasons, a worker node 3306 can generate additional partitionsand redistribute the data of the partition between the differentpartitions.

In some cases, if a worker node 3306 is assigned a large numberpartitions compared to other worker nodes 3306 or otherwise falls behindin processing the tasks or partitions, the worker nodes 3306 canredistribute partitions or tasks assigned to the worker node 3306amongst themselves. For example, the query coordinator 3304 candetermine that a significant number of results or partitions are beingsent or assigned to a particular worker node 3306 in the collector layer3608. As such, the query coordinator 3304 can allocate an additionalworker node 3306 to the collector layer and/or instruct that the resultsfrom the partitions in the processing layer 3606 be distributed in adifferent manner to reduce the load on the particular worker node 3306in the collector layer.

In certain embodiments, if a search layer is taking more time thanexpected, the query coordinator 3304 can allocate additional workernodes 3306 or processors 3406 to the layer to increase parallelism anddecrease the execution time. For example, the query coordinator 3304 candetermine that a worker node 3306 assigned to the processing layer 3606is not functioning or that there is significantly more data coming fromthe dataset source 3602 than was anticipated. Accordingly, the querycoordinator 3304 can allocate additional worker nodes 3306 or processors3406 to the intake layer 3604 or processing layer 3606. Conversely, ifthe query coordinator 3304 determines that some of the worker nodes 3306or processors 3406 are underutilized, then it can deallocate it from aparticular layer and make it available for other queries, or assign itto a different layer, etc. Accordingly, the query coordinator 3304 candynamically allocate and deallocate resources to intake and process thedata from the dataset source 3602 in a time-efficient and performantmanner.

As a non-limiting example, consider a query that includes a request tocount the number of different types of errors in data stored in anexternal data source within a timeframe and to return the results to theuser and store the results in the query acceleration data store 3308.Based on the query, the query coordinator 3304 can generate a DAG thatincludes the intake layer 3604, processing layer 3606, collector layer3608, branch layer 3610, and storage layer 3612. Additionally, based ona determination that the external data source supports four partitions,the query coordinator 3304 allocates four worker nodes 3306 orprocessors 3406 to the intake layer 3604 to process the data fromincoming partitions. In addition, based on the expected amount of datato be processed, the query coordinator 3304 allocates eight partitionsto the processing layer 3606, and five partitions to the collector layer3608. Further, based on resource availability and the determination thatthe dataset destination is the query acceleration data store 3308, whichcan support more than a threshold number of partitions, the querycoordinator 3304 allocates three worker nodes 3306 or processors to thestorage layer 3612 to process partitions at that layer. The taskinstructions for each search layer can be sent to the nodes 3306, whichassign processors 3406 to the various tasks and partitions.

During execution, the partitions in the intake layer 3604 (or processorsassigned to the partition) communicate with the dataset source 3602 toreceive the relevant data from the partitions of the dataset source3602. The data is then communicated to the partitions in the processinglayer 3606. In the illustrated embodiment, each worker node 3306 of theintake layer 3604 communicates data in a load-balanced fashion topartitions in the processing layer 3606. The worker nodes 3306 orprocessors 3406 in the processing layer 3606 can parse the incoming dataor partitions to identify events that include an error and identify thetype of error.

The worker nodes 3306 or processors 3406 in the processing layer 3606can communicate the results to partitions in the collector layer 3608.For example, one or more processors 3406 can apply a modulo five to theerror type to each partition in the processing layer 3606 in order toattempt to equally separate the results between the partitions in thecollector layer 3608. As such, for each error type, a partition (ormultiple related partitions) in the collector layer 3608 can include thetotal count of errors for that type. Depending on the query, in somecases, the partitions in the collector layer 3608 can also include theevent that included the particular error type.

The worker nodes 3306 or processors 3406 can send the results ofprocessing the partitions in the collector 3608 to a partition in thebranch layer 3610. The worker nodes 3306 or processors 3406 cancommunicate the results in the partition of the branch layer 3610 to thequery coordinator 3304, which can communicate the results to the searchhead or client device. In addition, the branch layer 3610 cancommunicate the results to the partitions in the storage layer 3612,which communicate the results in parallel to the query acceleration datastore 3308.

Throughout the execution of the query, the query coordinator 3304 canmonitor the worker nodes 3306 or processors 3406 processing partitionsin the intake layer 3604, processing layer 3606, collector layer 3608,branch layer 3610, and storage layer 3612. If a worker node 3306 orprocessor 3406 becomes unavailable or becomes overloaded, the querycoordinator 3304 can allocate additional resources or redistribute tasksor partitions. Similarly, if a worker node 3306 or processor 3406 is notbeing utilized, the query coordinator 3304 can deallocate it from alayer or redistribute the tasks or partitions. For example, if apartition on the external data source becomes unavailable, acorresponding worker node 3306 or processor 3406 in the intake layer3604 may no longer receive any data. As such, the query coordinator 3304can deallocate that worker node 3306 or processor 3406 from the intakelayer 3604. In some embodiments, any change in state of a worker node3306 or processor 3406 can be reported to the node monitor module 3314,which can be used by the query coordinator to allocate resources.

11.3.3. Result Processing

Once the nodes 3306 have completed processing the query or particularresults of the query, they can communicate the results to the querycoordinator 3304. The query coordinator 3304 can perform any finalprocessing. For example, in some cases, the query coordinator 3304 cancollate the data from the nodes 3306. The query coordinator 3304 canalso send the results to the search head 210 or to a datasetdestination. For example, based on a command (non-limiting example“into”), the query coordinator 210 can store results in the queryacceleration data store 3308, an external data source 3318, an ingesteddata buffer, etc. In addition, the query coordinator 3304 cancommunicate to the search process master 3302 that the query has beencompleted. In the event all queries assigned to the query coordinator3304 have been completed, the query coordinator can shut down or enter ahibernation state and await additional queries assigned to it by thesearch process master 3302.

11.4. Query Acceleration Data Store

As described herein, a query can indicate that information is to bestored (e.g., stored in non-volatile or volatile memory) in the queryacceleration data store 3308.

As described above, the query acceleration data store 3308 can storeinformation (e.g., datasets) sourced from other dataset sources, suchas, external data sources 3318, indexers 206, ingested data buffers,indexers, and so on. For example, when providing a query, a user canindicate that particular information is to be stored in the queryacceleration data store 3308 (e.g., cached). The information can includethe results of the query, partial results of the query, data (processedor unprocessed) received from another dataset source via the nodes 3306,etc. Subsequently, the data intake and query system 3301 can causequeries directed to the particular information to utilize the queryacceleration data store 3308. In this way, the stored information can berapidly accessed and utilized.

As an example, the query can indicate that information is to be obtainedfrom the external data sources 3318. Since the external data sources3318 may have potentially high latency, response times to particularqueries, the query can be constrained according to characteristics ofthe external data sources 3318. For example, particular external datasources 3318 may be limited in their processing speed, networkbandwidth, and so on, such that the worker nodes 3306 are required towait longer for information. As described herein, the query cantherefore specify that particular information from the external datasources 3318 (or other dataset sources) be stored in the queryacceleration data store 3308. Subsequent queries that utilize thisparticular information can then be executed more quickly. For example,in subsequent queries the worker nodes 3306 can obtain the particularinformation from the query acceleration data store 3308 rather than fromthe external data source 3318.

An example query can be of a particular form, such as:

Query=<from [dataset source]>|<[logic]>|[accelerated directive]

In the above example, the query indicates that information is to beobtained from a dataset source, such as an external data source 3318.Optionally, the query can indicate particular tables, documents,records, structured or unstructured information, and so on. As describedabove, the data intake and query system 3301 can process the query anddetermine that the external data source is being referenced. The nextelement of the query (e.g., a request parameter) includes logic to beapplied to the data from the external data source, for example the logiccan be implemented as structured query language (SQL), search processinglanguage (SPL), and so on. As described above, the worker nodes 3306 canobtain the requested data, and apply the logic to obtain information tobe provided in response to the query.

In the above example query, an accelerated directive is included. Forexample, the accelerated directive can be a particular term (e.g., “intoquery acceleration data store”), symbol, and so on, included in thequery. The accelerated directive can optionally be manually included inthe query (e.g., a user can type the directive), or automatically. As anexample of automatically including the directive, a user can indicate ina user interface associated with entering queries that information is tobe stored in the query acceleration data store 3308. As another example,the user's client device or query coordinator 3304 can determine thatinformation is to be stored in the data store 3308. For example, thequery can be analyzed by the client device or query coordinator 3304,and based on a quantity of information being requested, the clientdevice or query coordinator 3304 can automatically include theaccelerated directive (e.g., if greater than a threshold quantity isbeing requested, the directive can be included). Optionally, the dataintake and query system 3301 can automatically store the requestedinformation in the query acceleration data store 3308 without anaccelerated directive in a received query. For example, the query system3301 can automatically store data in the query acceleration data store3308 based on a user ID (e.g., always store results for a particularuser or based on recent use by the user), time of day (e.g., storeresults for queries made at the beginning or end of a work day, etc.),dataset source identity (e.g., store data from dataset source identifiedhas having a slower response time, etc.), network topology (e.g., storedata from sources on a particular network given the network bandwidth,etc.) etc. Although the above example shows the accelerated directive atthe end of the query, it will be understood that it can be placed at anypart of it. In some cases, the result of the command preceding theaccelerated directive corresponds to the data stored in the queryacceleration data store 3308.

Upon receipt of the query, the data intake and query system 3301 (e.g.,the query coordinator 3304) can cause the requested information from thedataset source to be stored in the query acceleration data store 3308.Optionally, the query acceleration data store 3308 can receive theprocessed result associated with the query (e.g., from the worker nodes3306). The query acceleration data store 3308 can then provide theprocessed result to the query coordinator 3304 to be relayed to therequesting client. However, to increase response times, the worker nodes3306 can provide processed information to the query acceleration datastore 3308, and also to the query coordinator 3304. In this way, thequery acceleration data store 3308 can store (e.g., in low latencymemory, or longer latency memory such as solid state storage or diskstorage) the received processed information, while the query coordinator3304 can relay the received processed information to the requestingclient.

The processed result may be stored by the query acceleration data store3308 in association with an identifier, such that the information can beeasily referenced. For example, the query acceleration data store 3308can generate a unique identifier upon receipt of information for storageby the worker nodes 3306. For subsequent queries, the query coordinator3304 can receive the identifier, such that the query coordinator 3304can replace the initial portion with the unique identifier.

In some embodiments, the query coordinator 3304 can generate the uniqueidentifier. For example, the query coordinator can receive informationfrom the query acceleration data store 3308 indicating that it storedinformation. The query coordinator 3304 can maintain a mapping betweengenerated unique identifiers and datasets, partitions, and so on, thatare associated with information stored by the query acceleration datastore 3308. The query coordinator 3304 may optionally provide a uniqueidentifier to the requesting client, such that a user of the requestingclient can re-use the unique identifier. For example, the user's clientcan present a list of all such identifiers along with respective queriesthat are associated with the identifier. The user can select anidentifier, and generate a new query that is based on an associatedquery.

In addition to storing the data or the results or partial results of thequery, the query acceleration data store can store additionalinformation regarding the results. For example, the query accelerationdata store can store information about the size of the dataset, thequery that resulted in the dataset, the dataset source of the dataset,the time of the query that resulted in the dataset, the time range ofdata that was processed to produce the dataset, etc. This informationcan be used by the system 3301 to prompt a user as to what data isstored and can be used in the query acceleration data store, determinewhether portions of an incoming query correspond to datasets in theaccelerate data store, etc. This information can also be stored in theworkload catalog 3312, or otherwise made available to the querycoordinator 3304.

Subsequently, for received queries that reference the processedinformation, the query coordinator 3304 can cause the worker nodes 3306to obtain the information from the query acceleration data store 3308.

For example, a subsequent query can be

Query=<from [dataset source]>|<[logic]>|<[subsequent_logic]>

In the above query, the query coordinator 3304 can determine that someportion of the data referenced in the query corresponds to data that isstored in the query acceleration data store 3308 (previously storeddata) or was previously processed according to a prior query (e.g., thequery represented above) and the results of the processing stored in thequery acceleration data store 3308. For example, the query coordinator3304 can compare the query to prior queries, and any portion of datathat was referenced in a prior query. The query coordinator 3304 canthen instruct the worker nodes 3306 to obtain the previously stored dataor the results of processing the data from the query acceleration datastore 3308. In some cases, the subsequent query can include an explicitcommand to obtain the data or results from the query acceleration datastore 3308.

Obtaining the previously stored data or results of processing the dataprovides multiple technical advantages. For example, the worker nodes3306 can avoid having to reprocess the data, and instead can utilize theprior processed result. Additionally, the worker nodes 3306 can morerapidly obtain information from the query acceleration data store 3308than, for example, the external data sources 3318. As an example, theworker nodes 3306 may be in communication with the query accelerationdata store 3308 via a direct connection (e.g., virtual networks, localarea networks, wide area networks). In contrast, the worker nodes 3306may be in communication with the external data sources 3318 via a globalnetwork (e.g., the internet).

As a non-limiting example, in some cases, a first query can indicatethat data from a dataset source is to be stored in the queryacceleration data store 3308 with minimal processing by the nodes 3306or without transforming the data from the dataset source. A subsequentquery can indicate that the data stored in the query acceleration datastore 3308 is to be processed or transformed, or combined with otherdata or results to obtain a result. In certain cases, the first querycan indicate that data from the dataset source is to be transformed andthe results stored in the query acceleration data store 3308. Thesubsequent query can indicate that the results stored in the queryacceleration data store 3308 are to be further processed, combined withdata or results from another dataset source, or provided to a clientdevice.

Furthermore, in certain embodiments, the worker nodes 3306 can performany additional processing on the results obtained from the queryacceleration data store 3308, while concurrently obtaining data fromanother dataset source and processing it to obtain additional results.In some cases, the results stored in the query acceleration data store3308 can be communicated to a client device while the nodes concurrentlyobtain data from another dataset source and process it to obtainadditional results. By obtaining, processing, and displaying the resultsof the previously processed data while concurrently obtaining additionaldata to be processed, processing the additional data, and communicatingthe results of processing the additional data, the system 3301 canprovide a more effective responsiveness to a user and decrease theresponse time of a query.

For the subsequent query identified above, the ‘subsequent_logic’ can beapplied by the worker nodes 3306 based on the processed result stored bythe query acceleration data store 3308. The result of the subsequentquery can then be provided to the query coordinator 3304 to be relayedto the requesting client.

The query acceleration data store 3308, as described herein, canmaintain information in low-latency memory (e.g., random access memory)or longer-latency memory. That is, the query acceleration data store3308 can cause particular information to spill to disk when needed,ensuring that the data store 3308 can service large amounts of queries.Since, in some implementations, the low-latency memory can be less thanthe longer-latency memory, the query acceleration data store 3308 candetermine which datasets are to be stored in the low-latency memory. Insome embodiments, to provide this functionality, the query accelerationdata store 3308 can be implemented as a distributed in-memory data storewith spillover to disk capabilities. For example, the data in the queryacceleration data store 3308 can be stored in low-latency volatilememory, and in the event, the capacity of the low-latency volatilememory is reached, the data can be stored to disk.

In some embodiments, the query acceleration data store 3308 can utilizeone or more storage policies to swap datasets between low-latency memoryand longer-latency memory. Additionally, the query acceleration datastore 3308 can flush particular datasets after determining that thedatasets are no longer needed (e.g., the user can indicate that thedatasets can be flushed, or a threshold amount of time can pass).

As an example of a storage policy, the query acceleration data store3308 can store a portion of a dataset in low-latency memory whilestoring a remaining portion in longer-latency memory. In this way, thequery acceleration data store 3308 can have faster access to at least aportion each user's dataset. If a subsequent query is received by thedata intake and query system 3301 that references a stored dataset, thequery acceleration data store 3308 can access the portion of the storeddataset that is in low-latency memory. Since this access is, in general,with low-latency, the query acceleration data store 3308 can quicklyprovide this information to the worker nodes 3306 for processing. At asame, or similar, time, the query acceleration data store 3308 canaccess the longer-latency memory and obtain a remaining portion of thestored dataset. The worker nodes 3306 can then receive this remainingportion for processing. Therefore, the worker nodes 3306 can quicklyrespond to a request, based on the initially received portion from thelow-latency memory. In this way, the user can receive search results ina manner that appears to be in ‘real-time’, that is, the search resultscan be provided in a less than a threshold amount of time (e.g., 1second, 5 seconds, 10 seconds). Subsequent search results can then beprovided upon the worker nodes 3306 processing the portion from thelonger-latency memory.

The above-described storage policy may be based on a size of thedataset(s). For example, an example dataset may be less than athreshold, and the query acceleration data store 3308 may store theentirety of the dataset in low-latency memory. For an example datasetgreater than the threshold, the data store 3308 may store a portion inlow-latency memory. As the size of the dataset increases, the queryacceleration data store 3308 can store an increasingly lesser sizedportion in low-latency memory. In this way, the data store 3308 canensure that large data sets do not consume the low-latency memory.

While the queries described above indicate, a first query that includesan accelerated directive, and a second query that includes the firstquery (e.g., as an initial portion), optionally the data intake andquery system 3301 can receive a first query that is a combination of thefirst query and second query described above. For example, an exampleinitial query can be

Query=<from [dataset source]>|<[logic]>|[accelerateddirective]|<[subsequent_logic]>

The above example query indicates that the data intake and query system3301 is to obtain information from an example dataset source (e.g.,external data source 3318), process the information, and cause the queryacceleration data store 3308 to store the processed information. Inaddition, subsequent logic is to be applied to the processedinformation, and the result provided to the requesting client 404 a-404n.

FIG. 36 illustrates a branch layer 3610, which for the example querydescribed above, can be utilized to provide information both to thequery acceleration data store 3308 and the data destination 3614 (e.g.,the requesting client). For example, subsequent to the worker nodes 3306obtaining processed information (e.g., based on the dataset source andlogic), the worker nodes 3306 can provide the processed information forstorage in the query acceleration data store 3308 while continuing toprocess the query (e.g., apply the subsequent logic). That is, theworker nodes 3306 can bifurcate the data (e.g., at branch layer 3610),such that the query acceleration data store 3308 can store partialresults while the worker nodes 3306 service the query and provide thecompleted results to the query coordinator 3304. Optionally, anotherquery may be received that references the partial results in the datastore 3308, and one or more worker nodes 3306 may access the data store3308 to service the other query. For example, the other query may beprocessed at a same time as the above-described example initial query.

Received queries can further indicate multiple datasets stored by thequery acceleration data store 3308. For example, a first query canindicate that first information is to be obtained (e.g., from externaldata source 3318, indexers 206, common storage, and so on) and stored inthe query acceleration data store 3308 as a first dataset. Additionally,a second query can indicate that second information is to obtained andstored in the data store 3308 as a second dataset. Subsequent queriescan then reference the stored first dataset and second dataset, suchthat logic can be applied to both the first and second dataset via rapidaccess to the query acceleration data store 3308.

Furthermore, queries can reference datasets stored by the queryacceleration data store 3308, and also datasets to be obtained fromanother dataset source (e.g., from external data source 3318, indexers206, ingested data buffer, and so on). For particular queries, the dataintake and query system 3301 may be able to provide results (e.g.,search results) from the query acceleration data store 3308 whiledatasets is being obtained from another dataset source. Similarly, thesystem 3301 may be able to provide results from the data store 3308while data obtained from another dataset source is being processed.

As an example, a first query can cause a dataset to be stored in thequery acceleration data store 3308, with the dataset being from anexternal data source 3318 and representing records from a prior timeperiod (e.g., one hour). Subsequently, a second query can reference thestored dataset and further cause newer records to be obtained from theexternal data source (e.g., a subsequent hour). For this second query,particular logic indicated in the second query can enable the dataintake and query system 3301 to provide results to a requesting clientbased on the stored dataset in the query acceleration data store 3308.As an example, the second query can indicate that the system 3301 is tosearch for a particular name. The worker nodes 3306 can obtain storedinformation from the query acceleration data store 3308, and identifyinstances of the particular name.

This access to the query acceleration data store 3308, as describedabove, can be low-latency. For example, the query acceleration datastore 3308 may have a portion of the stored information in low-latencymemory, such as RAM or volatile memory, and the worker nodes 3306 canquickly obtain the information and identify instances of the particularname. These identified instances can then be relayed to the requestingclient. Similarly, the query acceleration data store 3308 may have adifferent portion of the stored information in longer-latency memory,and can similarly identify instances of the particular name to beprovided to the requesting client.

The above-described worker node 3306 interactions with the queryacceleration data store 3308 can occur while information is beingobtained, or processed, from the external data source 3318 referenced bythe second query. In this way, the requesting client can view searchresults, for example search results based on the dataset stored by thequery acceleration data store 3308, while subsequent search results arebeing determined (e.g., search results based on information from adifferent dataset source). Furthermore, and as described above, thedataset being obtained from the other dataset source can be provided tothe query acceleration data store 3308 for storage, for example,provided while the worker nodes 3306 apply logic to determine resultsfrom the obtained dataset.

To increase security of the datasets stored by the query accelerationdata store, access controls can be implemented. For example, eachdataset can be associated with an access control list, and the querycoordinator 3304 can provide an identification of a requesting user tothe worker nodes 3306 and/or query acceleration data store 3308. Forexample, the identification can be an authorization or authenticationtoken associated with the user. The query acceleration data store 3308can then ensure that only authorized users are allowed access to storeddatasets. For example, a user who causes a dataset to be stored in thequery acceleration data store 3308 (e.g., based on a provided query) canbe indicated as being authorized (e.g., in an access control listassociated with the dataset). Optionally, the user can indicate one ormore other users as having access. Optionally, the data intake and querysystem 3301 can utilize role-based access controls to allow any userassociated with a particular role to access particular datasets. In thisway, the stored information can be secure while enabling the queryacceleration data store 3308 to service multitudes of users.

12.0. Query Data Flow

FIG. 37 is a data flow diagram illustrating an embodiment ofcommunications between various components within the environment 3300 toprocess and execute a query. At (1), the search head 210 receives andprocesses a query. At (2), the search head 210 communicates the query tothe search process service 3702, which can refer to the search processmaster 3302 and/or query coordinator 3304.

At (3) the search process service processes the query. As described ingreater detail above, as part of processing the query, the querycoordinator 3304 can identify the dataset sources (e.g., external datasources 3318, indexers 206, query acceleration data store 3308, commonstorage, ingested data buffer, etc.) to be accessed, generateinstructions for the dataset sources based on their processingcapabilities or communication protocols, determine the size of thequery, determine the amount of resources to allocate for the query,generate instructions for the nodes 3306 to execute the query, andgenerate tasks for itself to process results from the nodes 3306.

At (4), the query coordinator 3304 communicates the task instructionsfor the query to the worker nodes 3306 and/or the dataset sources 3704.As described above, in some embodiments, the query coordinator 3304 cancommunicate task instructions to the dataset sources 3704. In certainembodiments, the nodes 3306 communicate task instructions to the datasetsources 3704.

At (5), the nodes 3306 and/or dataset sources 3704 process the receivedinstructions. As described in greater detail above, the instructions forthe dataset sources 3704 can include instructions for performing certaintransformations on the data prior to communicating the data to the nodes3306, etc. As described in greater detail above, the instructions forthe nodes 3306 can include instructions on how to access the relevantdata, the number of search phases or layers to be generated, the numberof partitions, worker nodes 3306, or processors 3406 to be allocated foreach search phase or layer, the tasks for the partitions or processors3406 in the different layer, data routing information to route databetween the nodes 3306 and to the search process service 3702, etc. Assuch, based on the received instructions, the nodes 3306 can assignprocessors 3406 to different layers and partitions and begin executingthe task instructions.

At (6), the nodes 3306 receive the data from the dataset source(s). Asdescribed in greater detail above, the nodes 3306 can receive the datafrom one or more dataset sources 3704 in parallel. In addition, thenodes 3306 can receive the data from a dataset source using one or morepartitions or processors 3406. The data received from the datasetsources 3704 can be semi-processed data based on the processingcapabilities of the dataset source 3704 or it can be unprocessed datafrom the dataset source 3704.

At (7), the nodes 3306 process the data based on the task instructionsreceived from the query coordinator 3304. As described in greater detailabove, the nodes 3306 can process the data using one or more layers,each having one or more partitions or processors 3406 assigned thereto.Although not illustrated in FIG. 37 , it will be understood that thesearch process service 3702 can monitor the nodes 3306 and dynamicallyallocate resources based on the monitoring.

At (8), the nodes 3306 communicate the results of the processing to thequery coordinator 3304 and/or to a dataset destination 3704. In somecases the dataset destination 3704 can be the same as the datasetsource. For example, the nodes 3306 can obtain data from the ingesteddata buffer and then return the results of the processing to a differentsection of the ingested data buffer, or obtain data from the queryacceleration data store 3308 or an external data source 3318 and thenreturn the results of the processing to the query acceleration datastore 3308 or external data source 3318, respectively. However, incertain embodiments, the dataset destination 3704 can be different fromthe dataset source 3704. For example, the nodes 3306 can obtain datafrom the ingested data buffer and then return the results of theprocessing to the query acceleration data store 3308 or an external datasource 3318.

At (9), the search process service 3702 can perform additionalprocessing, and at (10) the results can be communicated to the searchhead 210 for communication to the client device. In some cases, prior tocommunicating the results to the client device, the search head 210 canperform additional processing on the results.

It will be understood that the query data flow can include fewer or moresteps. For example, in some cases, the search process service 3702 doesnot perform any further processing on the results and can simply forwardthe results to the search head 210. In certain embodiments, nodes 3306receive data from multiple dataset sources 3704, etc.

13.0. Query Coordinator Flow

FIG. 38 is a flow diagram illustrative of an embodiment of a routine3800 implemented by the query coordinator 3304 to provide query results.Although described as being implemented by the query coordinator 3304,it will be understood that one or more elements outlined for routine3800 can be implemented by one or more computing devices/components thatare associated with the system 3301, such as the search head 210, searchprocess master 3302, indexer 206, and/or worker nodes 3306. Thus, thefollowing illustrative embodiment should not be construed as limiting.

At block 3802, the query coordinator 3304 receives a query. As describedin greater detail above, the query coordinator 3304 can receive thequery from the search head 210, search process master 3302, etc. In somecases, the query coordinator 3304 can receive the query from a client404. The query can be in a query language as described in greater detailabove. In some cases, the query received by the query coordinator 3304can correspond to a query received and reviewed by the search head 210.For example, the search head 210 can determine whether the query wassubmitted by an authenticated user and/or review the query to determinethat it is in a proper format for the data intake and query system 3301,has correct semantics and syntax, etc. In some cases, the search head210 can run a daemon to receive search queries, and in some cases, spawna search process, to communicate the received query to and receive theresults from the query coordinator 3304 or search process master 3302

At block 3804, the query coordinator 3304 processes the query. Asdescribed in greater detail above and as will be described in greaterdetail in FIG. 39 , processing the query can include any one or anycombination of: identifying relevant dataset sources and destinationsfor the query, obtaining information about the dataset sources anddestinations, determining processing tasks to execute the query,determining available resources for the query, and/or generating a queryprocessing scheme to execute the query based on the information. In someembodiments, as part of generating a query processing scheme, the querycoordinator 3304 allocates multiple layers or search phases ofpartitions or processors 3406 to execute the query. Each level or phasecan be given a different task in order to execute the query. Forexample, as described in greater detail above with reference to FIGS. 20and 21 , one level can be given the task of interacting with the datasetsource and receiving data from the dataset source, another level can betasked with processing the data received from the dataset source, athird level can be tasked with collecting results of processing thedata, and additional levels can be tasked with communicating results todifferent destinations, storing the results in one or more datasetdestinations, etc. The query coordinator 3304 can allocate as many or asfew levels of partitions or processors 3406 to execute the query.

At block 3806, the query coordinator 3304 distributes the query forexecution. Distributing the query for execution can include any one orany combination of: communicating the query processing scheme to thenodes 3306, monitoring the nodes 3306 during the processing of thequery, or allocating/deallocating resources based on the status of thenodes and the query, and so forth, as described herein.

At block 3808, the query coordinator 3304 receives the results. In someembodiments, the query coordinator 3304 receives the results from thenodes 3306. For example, upon completing the query processing scheme, oras a part of it, the nodes 3306 can communicate the results of the queryto the query coordinator 3304. In certain cases, the query coordinator3304 receives the results from the query acceleration data store, orindexers 206, etc. In some cases, the query coordinator 3304 receivesthe results from one or more components of the data intake and querysystem 3301 depending on the dataset sources used in the query.

At block 3810, the query coordinator 3304 processes the results. Asdescribed in greater detail above, in some cases, the results of a querycannot be finalized by the nodes 3306. For example, in some cases, allof the data must be gathered before the results can be determined. As anon-limiting example, for some cursored searches, a result cannot bedetermined until all relevant data has been collected by the workernodes. In such cases, the query coordinator 3304 can receive the resultsfrom the worker nodes 3306, and then collate the results.

At block 3812, the query coordinator 3304 communicates the results. Insome embodiments, the query coordinator 3304 communicates the results tothe search head 210, such as a search process generated by the search tohandle the query. In certain cases, the query coordinator 3304communicates the results to the search process master 3302 or clientdevice 404, etc.

It will be understood that fewer, more, or different blocks can be usedas part of the routine 3800. In some cases, one or more blocks can beomitted. For example, in certain embodiments, the results received fromnodes 3306 can be in a form that does not require any additionalprocessing by the query coordinator 3304. In such embodiments, the querycoordinator 3304 can communicate the results without additionalprocessing. As another example, the routine 3800 can include monitoringnodes during execution of the query or query processing scheme,allocating or deallocating resources during the execution of the query,etc. Similarly, routine 3800 can include reporting completion of thequery to a component, such as the search process master 3302, etc.

Furthermore, it will be understood that the various blocks describedherein with reference to FIG. 38 can be implemented in a variety oforders. In some cases, the query coordinator 3304 can implement someblocks concurrently or change the order as desired. For example, thequery coordinator 3304 can receive (3808), process (3810), and/orcommunicate results (3812) concurrently or in any order, as desired.

14.0. Query Processing Flow

FIG. 39 is a flow diagram illustrative of an embodiment of a routine3900 implemented by the query coordinator 3304 to process a query.Although described as being implemented by the query coordinator 3304,it will be understood that one or more elements outlined for routine3900 can be implemented by one or more computing devices/components thatare associated with the system 3301, such as the search head 210, searchprocess master 3302, indexer 206, and/or worker nodes 3306. Thus, thefollowing illustrative embodiment should not be construed as limiting.

At block 3902, the query coordinator 3304 identifies dataset sourcesand/or destinations for the query. In some cases, the query explicitlyidentifies the dataset sources and destinations that are to be used inthe query. For example, the query can include a command indicating thatdata is to be retrieved from the query acceleration data store 3308,ingested data buffer, common storage, indexers, or an external datasource (inclusive of external data systems 12). In certain cases, thequery coordinator 3304 parses the query to identify the dataset sourcesand destinations that are to be used in the query. For example, thequery may identify the name (or other identifier) or the location (e.g.,my_index) of the relevant data and the query coordinator 3304 can usethe name or identifier to determine whether that particular location isassociated with the query acceleration data store 3308, ingested databuffer, common storage, indexers 206, or an external data source 3318.

In certain embodiments, the query can include a reference or identifierthat can be used to look up or otherwise identify the dataset source.For example, the query can include a reference to an external queryconfiguration file that includes information about dataset sources, etc.In some cases, the external query configuration file can include detailsabout the dataset source, such as, but not limited to, an identifier forthe dataset source, search type to be performed on the dataset source(e.g., streaming, batch, reporting, etc.), maximum or estimate number(or size) of results expected from the dataset source, number IPaddress, port number, access credentials (e.g., account name/type,password, etc. to access the dataset source), etc.

In some cases, the query coordinator identifies the dataset source basedon timing requirements of the search. For example, in some cases,queries for data that satisfy a timing threshold or are within a timeperiod are handled by indexers or correspond to data in an ingested databuffer, as described herein. In some embodiments, data that does notsatisfy the timing threshold or is outside of the time period are storedin common storage, query acceleration data stores, external datasources, or by indexers. For example, as described in greater detailherein, in some cases, the indexers fill hot buckets with incoming data.Once a hot bucket is filled, it is stored. In some embodiments hotbuckets are searchable and in other embodiments hot buckets are not.Accordingly, in embodiments where hot buckets are searchable, a querythat reflects a time period that includes hot buckets can indicate thatthe dataset source is the indexers, or hot buckets being processed bythe indexers. Similarly, in embodiments where warm buckets are stored bythe indexers, a query that reflects a time period that includes warmbuckets can indicate that the dataset source is the indexers.

In certain embodiments, a query for data that satisfies the timingthreshold or is within the time period can indicate that the ingesteddata buffer is the dataset source. Further, in embodiments, where warmbuckets are stored in a common storage, a query for data that does notsatisfy the timing threshold or is outside of the time period canindicate that the common storage is the dataset source. In someembodiments, the time period can be reflective of the time it takes fordata to be processed by the data intake and query system 3301 and storedin a warm bucket. Thus, a query for data within the time period canindicate that the data has not yet been indexed and stored by theindexers 206 or that the data resides in hot buckets that are stillbeing processed by the indexers 206.

In some embodiments, the query coordinator 3304 identifies the datasetsource based on the architecture of the system 3301. As describedherein, in some architectures, real-time searches or searches for datathat satisfy the timing threshold are handled by indexers. In otherarchitectures, these same types of searches are handled by the nodes3306 in combination with the ingested data buffer. Similarly, in certainarchitectures, historical searches, or searches for data that do notsatisfy the timing threshold are handled by the indexers. In otherarchitectures, these same types of searches are handled by the nodes3306 in combination with the common storage.

At block 3904 the query coordinator 3304 obtains relevant informationabout the dataset sources/destinations. The query coordinator 3304 canobtain the relevant information from a variety of sources, such as theworkload advisor 3310, workload catalog 3312, dataset compensationmodule 3316, the dataset sources/destinations themselves, etc. Forexample, if the dataset source/destination is an external data source,the query coordinator 3304 can obtain relevant information about theexternal dataset source 3318 from the dataset compensation module or bycommunicating with the external data source 3318. Similarly, if thedataset source/destination is an indexer 206, common storage, queryacceleration data store 3308, ingested data buffer, etc., the querycoordinator can obtain relevant information by communicating with thedataset source/destination and/or the workload advisor 3310 or workloadcatalog 3312.

The relevant information can include, but is not limited to, informationto enable the query coordinator 3304 to generate a search scheme withsufficient information to interact with and obtain data from a datasetsource or send data to a dataset destination. For example, the relevantinformation can include information related to the number of partitionssupported by the dataset source/destination, location of compute nodesat the dataset source/destination, computing functionality of thedataset source/destination, commands supported by the datasetsource/destination, physical location of the dataset source/destination,network speed and reliability in communicating with the datasetsource/destination, amount of information stored by the datasetsource/destination, computer language or protocols for communicatingwith the dataset source/destination, summaries or indexes of data storedby the dataset source/destination, data format of data stored by thedataset source/destination, etc.

At block 3906, the query coordinator 3304 determines processingrequirement for the query. In some cases, to determine the processingrequirements, the query coordinator 3304 parses the query. As describedpreviously, the workload catalog 3312 can store information regardingthe various transformations or commands that can be executed on data andthe amount of processing to perform the transformation or command. Insome cases, this information can be based on historical information fromprevious queries executed by the system 3301. For example, the querycoordinator 3304 can determine that a “join” command will havesignificant computational requirements, whereas a “count by” command maynot. Using the information about the transformations included in thequery, the query coordinator can determine the processing requirementsof individual transformations on the data, as well as the processingrequirements of the query.

At block 3908, the query coordinator 3304 determines availableresources. As described in greater detail above, the nodes 3306 caninclude monitoring modules that monitor the performance and utilizationof its processors. In some cases, a monitoring module can be assignedfor each processor on a node. The information about the utilization rateand other scheduling information can be used by the query coordinator3304 to determine the amount of resources available for the query.

At block 3910, the query coordinator 3304 generates a query processingscheme. In some cases, the query coordinator 3304 can use theinformation regarding the dataset sources/destinations, the processingrequirements of the query and/or the available resources to generate thequery processing scheme. As part of generating the query processingscheme, the query coordinator 3304 can generate instructions to beexecuted by the dataset sources/destinations, allocatepartitions/processors for the query, generate instructions for theprocessors/nodes, generate instructions for itself, generate a DAG, etc.

As described in greater detail above, in some embodiments, to generateinstructions for the dataset sources/destinations, the query coordinator3304 can use the information from the dataset compensation module 3316.This information can be used by the query coordinator 3304 to determinewhat processing can be done by an external data source, how to translatethe commands or subqueries for execution to the external dataset source,the number of partitions, worker nodes 3306, or processors 3406 that canbe used to read data from the external dataset source, etc. Similarly,the query coordinator 3304 can generate instructions for other datasetsources, such as the indexers, query acceleration data store, commonstorage, etc. For example, the query coordinator 3304 can generateinstructions for the ingested data buffer to retain data until itreceives an acknowledgment from the query coordinator that the data fromthe ingested data buffer has been received and processed.

In addition, as described in greater detail above, to generateinstructions for the processors/nodes, the query coordinator 3304 candetermine how to break up the processing requirements of the query intodiscrete or individual tasks, determine the number ofpartitions/processors to execute the task, etc. In some cases, todetermine how to break up the processing requirements of the query intodiscrete or individual tasks, the query coordinator 3304 can parse thequery into its different portions of the query and then determine thetasks to use to execute the different portions.

The query coordinator 3304 can then use this information to generatespecific instructions for the nodes that enable the nodes to execute theindividual tasks, route the results of each task to the next location,and route the results of the query to the proper destination. Theinstructions for the nodes can further include instructions forinteracting with the dataset sources/destinations. In some cases,instructions for the dataset sources can be embedded in the instructionsfor the nodes so that the nodes can communicate the instructions to thedataset sources/destinations. Accordingly, the instructions generated bythe query coordinator 3304 for the nodes can include all of theinformation in order to enable the nodes to handle the various tasks ofthe query and provide the query coordinator with the appropriate data sothat the query coordinator 3304 can finalize the results and communicatethem to the search head 210.

In some cases, the query coordinator 3304 can use network topologyinformation of the machines that will be executing the query to generatethe instructions for the nodes. For example, the query coordinator 3304can use the physical location of the processors that will execute thequery to generate the instructions. As one example, the querycoordinator 3304 can indicate that it is preferred that the processorsassigned to execute the query be located on the same machine or close toeach other.

In some embodiments, the instructions for the nodes can be generated inthe form of a DAG, as described in greater detail above. The DAG caninclude the instructions for the nodes to carry out the processing tasksincluded in the DAG. In some cases, the DAG can include additionalinformation, such as instructions on how to select processors 3406 forthe different tasks or distribute partitions. For example, the DAG canindicate that it is preferable that a partition that will be receivingdata from another partition be on the same machine, or nearby machine,in order to reduce network traffic.

In addition to generating instructions for the datasetsources/destinations and the nodes, the query coordinator 3304 cangenerate instructions for itself. In some cases, the instructionsgenerated for itself can depend on the query that is being processed,the capabilities of the nodes 3306, and the results expected from thenodes. For example, in some cases, the type of query requested mayrequire the query coordinator 3304 to perform more or less processing.For example, a cursored search may require more processing by the querycoordinator 3304 than a batch search. Accordingly, the query coordinator3304 can generate tasks or instructions for itself based on the queryrequested.

In addition, if the nodes 3306 are unable to perform certain tasks onthe data, then the query coordinator 3304 can assign those tasks toitself and generate instructions for itself based on those tasks.Similarly, based on the form of the data that the query coordinator 3304is expected to receive, it can generate instructions for itself in orderto finalize the results for reporting.

It will be understood that fewer, more, or different blocks can be usedas part of the routine 3900. In some cases, one or more blocks can beomitted. Furthermore, it will be understood that the various blocksdescribed herein with reference to FIG. 39 can be implemented in avariety of orders. In some cases, the query coordinator 3304 canimplement some blocks concurrently or change the order as desired. Forexample, the query coordinator 3304 can obtain information about thedataset sources/destinations (3904), determine processing requirements(3906), and determine available resources (3908) concurrently or in anyorder, as desired.

15.0. Workload Monitoring and Advising Flow

FIG. 40 is a flow diagram illustrative of an embodiment of a routine4000 implemented by the system 3301 to generate a query processingscheme. It will be understood that one or more elements outlined forroutine 4000 can be implemented by one or more computingdevices/components that are associated with the system 3301, such as thesearch head 210, search process master 3302, query coordinator 3304,indexer 206, and/or worker nodes 3306. Thus, the following illustrativeembodiment should not be construed as limiting.

At block 4002, the system 3301 tracks query-resource usage data. Asdescribed in greater detail above, the system 3301 can track detailedinformation related to queries that are executed by the system 3301,which in some embodiments can be stored in the workload catalog 3312, orotherwise stored to be accessible to the system 3301. For example, thesystem can track data indicating the resources used to execute thequeries or timing information indicating the amount of time a query tookto execute. Furthermore, the system can track information on a pertransformation level, indicating the resources used to perform aparticular task or transformation on a set of data, the amount of datainvolved, the time it took to perform the transformation, etc. In someembodiments, this information and other information related to previousqueries, datasets, and system components can be stored in the workloadcatalog 3312.

At block 4004, the system 3301 tracks resource utilization data. Asdescribed in greater detail above, the system 3301 can track detailedinformation related to utilization rates of system resources, which insome cases can be stored in the node monitoring module 3314. In someembodiments, the nodes 3306 can include monitoring modules 3410, whichcan monitor the utilization rates of processors, I/O, memory, and othercomponents of the nodes 3306. The information from the nodes 3306 of thesystem 3301 can be communicated to the node monitoring module 3314 forstorage. In some cases, each node 3306 can include at least onemonitoring module 3410. In certain embodiments, each node 3306 caninclude at least one monitoring module for each processor 3406 of thenode 3306.

At block 4006, the system 3301 receives a query, as described in greaterdetail above. At block 4008, the system 3301 defines a query processingscheme, as described in greater detail above. In some cases the system3301 can use the query-resource usage data and/or the resourceutilization data to define the query processing scheme.

In some embodiments, the system 3301 can use the query-resource usagedata to determine the amount of time the query will take to completecompared to the amount of resources assigned to process the query. Thesystem can use this information to determine an amount of resources toallocate based on query. For example, the system can compare thedatasets used for the received query with datasets used for previousqueries, the types of transformations required by the received querycompared to previous queries. Based on the comparison, the system 3301can determine the effect of the amount of resources assigned to thequery compared to the time to execute the query.

In certain embodiments, the system 3301 can further use the resourceutilization data to define the query processing scheme. For example, thesystem 3301 can determine the amount of resources that are currentlyavailable for use to execute the query. Based on the amount of currentlyavailable resources, the system 3301 can determine how many resourcesshould be allocated to the query. As an example, assume that based onthe query-resource usage data, the system 3301 determines that thirtyprocessors are preferred to process a query and that fewer than twentyprocessors would result in an undue delay. Based on the system 3301determining that thirty processors are available, the system 3301 canallocate all thirty processors or at least twenty for the query.

In some cases, the system 3301 can track usage over time to predictsurges in queries or determine whether additional queries are expectedin the near term. For example, the system 3301 may determine that thereis a surge in queries around 9:00 AM when most users begin work. Withcontinued reference to the example above, if the query is received at8:55 AM and the thirty processors are available, the system 3301 maydetermine to allocate twenty processors rather than the preferred thirtybecause a large number of queries are expected at 9:00 AM.

At block 4010, the system executes the query. In some cases, asdescribed in greater detail above, to execute the query, the systemcommunicates a query processing scheme to the nodes 3306. In turn, thenodes obtain relevant data from the datasets, process the data, andreturn results to the query coordinator. The query coordinator performsany additional processing based on the query processing scheme andcommunicates the results to the search head 210 for display on theclient device 404.

It will be understood that fewer, more, or different blocks can be usedas part of the routine 4000. For example, in some embodiments, theroutine 4000 can further include, monitoring nodes during queryexecution, allocating/deallocating resources based on the query,Furthermore, it will be understood that the various blocks describedherein with reference to FIG. 40 can be implemented in a variety oforders. In some cases, the system 3301 can implement some blocksconcurrently or change the order as desired. For example, the system3301 can track query-resource usage data 4002, track resourceutilization of nodes 4004, and receive a query 4006 concurrently or inany order, as desired. Similarly, the system 3301 can track resourceutilization of nodes 4004 while executing the query 4010, etc.

16.0. Multiple Dataset Sources Flow

FIG. 41 is a flow diagram illustrative of an embodiment of a routine4100 implemented by the query coordinator 3304 to execute a query ondata from multiple dataset sources. Although described as beingimplemented by the query coordinator 3304, it will be understood thatone or more elements outlined for routine 4100 can be implemented by oneor more computing devices/components that are associated with the system3301, such as the search head 210, search process master 3302, indexer206, and/or worker nodes 3306. Thus, the following illustrativeembodiment should not be construed as limiting.

At block 4102, the query coordinator 3304 receives a query, as describedin greater detail above with reference to block 3802 of FIG. 38 . Atblock 4104, the query coordinator identifies the dataset sources,including the indexers 206 as one dataset source, as described ingreater detail above with reference to block 3902 of FIG. 39 . The querycoordinator 3304 can also identify a second dataset source, such as anexternal data source, a common storage, an ingested data buffer, queryacceleration data store, etc.

At block 4106, the query coordinator 3304 generates a subquery for theindexers. As described herein, the subquery can be generated based onthe processing capabilities of the indexers. The subquery can indicateto the indexers that data to be processed by the indexers and the mannerof processing the data by the indexers. Further, the subquery caninstruct the indexers to provide the results (or partial results) of thesubquery to the nodes 3306 for further processing. Accordingly, usingthe subquery, the indexers can identify the data to process, process thedata, and communicate the results to the nodes 3306. The subquery can bein any query language, as described herein.

At block 4108, the query coordinator 3304 allocates resources, such aspartitions, worker nodes 3306, or processors 3406, for a second dataset.The resource allocation can be based on the information about thedataset and/or the query requirements, as described in greater detail inblocks 3906, 3908, and 3910 of FIG. 39 . At block 4110, the querycoordinator 3304 determines or allocates resources to combine theresults (or partial results) from the two datasets. Similar to block4108, the query coordinator 3304 can determine or allocate partitions,worker nodes 3306, or processors 3406 to combine the partial resultsfrom the different datasets based on the query requirements. Forexample, the query can include a command indicating that the resultsfrom different dataset sources are to be combined in some way.

At block 4112, the query coordinator 3304 executes the query asdescribed in greater detail above with reference to block 4010 of FIG.40 . In executing the query, the query coordinator 3304 can communicatethe subquery to the indexers 206 or embed the subquery into theinstructions to the nodes 3306 such that the nodes 3306 communicate thesubquery to the indexers 206.

It will be understood that fewer, more, or different blocks can be usedas part of the routine 4100. For example, in some embodiments, theroutine 4100 can further include, monitoring nodes during queryexecution, allocating/deallocating resources based on the query, etc. Asanother example, in certain embodiments, the identification of thedataset sources, generation of a subquery and resource allocation canform part of a processing query block, similar to the process queryblock 3804 of FIG. 38 . In some cases, the routine 4100 can includeallocating resources to receive and process the partial results from theindexers 206 prior to combining the partial results from the differentdatasets. In certain embodiments, the system 3301 can dynamicallyallocate resources based on the number of indexers 206 from which thenodes 3306 will receive data. Furthermore, although described asinteracting with indexers 206, it will be understood that the system3301 can process and execute the query on any two or more datasetsources, and that the system 3301 can generate subqueries orinstructions for the dataset sources or allocate resources for thedataset sources based on information about the dataset sources, asdescribed in greater detail herein.

Furthermore, it will be understood that the various blocks describedherein with reference to FIG. 41 can be implemented in a variety oforders. In some cases, the system 3301 can implement some blocksconcurrently or change the order as desired. For example, the system3301 can generate a subquery for the indexers 4106, allocate resourcesfor the second dataset 4108, and allocate resources to combine partialresults from the indexers and second dataset 4110 concurrently, or inany order, as desired.

17.0. External Data Source Flow

FIG. 42 is a flow diagram illustrative of an embodiment of a routine4200 implemented by the query coordinator 3304 to execute a query ondata from an external data source. Although described as beingimplemented by the query coordinator 3304, it will be understood thatone or more elements outlined for routine 4200 can be implemented by oneor more computing devices/components that are associated with the system3301, such as the search head 210, search process master 3302, indexer206, and/or worker nodes 3306. Thus, the following illustrativeembodiment should not be construed as limiting.

At block 4202, the query coordinator 3304 receives a query, as describedin greater detail above with reference to block 3802 of FIG. 38 . Atblock 4204, the query coordinator identifies the external data sources,as described in greater detail above with reference to block 3902 ofFIG. 39 .

At block 4206, the query coordinator 3304 dynamically generates asubquery for the external data source. As described herein, the querycoordinator 3304 can generate the subquery for the external data sourcebased on information obtained about the external data source asdescribed herein with reference to, inter alia, blocks 3904 and 3910 ofFIG. 39 . In certain embodiments, the query coordinator 3304 obtainsinformation about the external data source using an external queryconfiguration file, as described herein at least with reference to FIGS.50A, 50B, 51, 52, 54, and 61 . The information can indicate the type ofexternal data source, APIs and languages to use to interface with theexternal data source, the type and amount of data stored in the externaldata source. In addition, the information can indicate whether theexternal data source supports multiple partitions, and if so, how many.Further, the information can indicate the location of the processors ofthe external data source with which the nodes 3306 will interact. Theinformation can also indicate the processing capabilities of theexternal data source, such as what commands or transformations theexternal data source can perform on the data stored therein.

Using the information about the external data source, the querycoordinator 3304 can generate a subquery. In certain embodiments, thequery coordinator 3304 generates a subquery that tasks the external datasource with merely returning the data, performing some processing of thedata, or processing the data as much as it can based on itscapabilities. By pushing some processing of the data to the externaldata source, the query coordinator 3304 can reduce the processing loadon the system 3301.

At block 4208, the query coordinator 3304 allocates resources, such as,but not limited to, partitions, worker nodes 3306, or processors 3406 toreceive and process results from the external data source. As describedherein, the query coordinator 3304 can allocate resources based on thequery requirements and the data received from the external data source.For example, if the external data source can perform some processing onthe data, then the query coordinator 3304 can allocate resources toreceive the results of the processing. If the subquery indicated thatthe external data source was to return results without processing them,then the query coordinator 3304 can allocate resources to receive theunprocessed results from the external data source, and process themaccording to the query.

In addition, the query coordinator 3304 can allocate resources based onthe number of partitions supported by the external data source. Forexample, if the external data source supports four partitions forreading data, then the query coordinator 3304 can allocate four workernodes 3306 or processors 3406 to read from each of the partitionssupported by the external data source. However, it will be understoodthat the query coordinator 3304 can allocate fewer or more worker nodes3306 or processors 3406 as desired. Further, the number of worker nodes3306 or processors 3406 allocated can be based on the resourcesavailable on the system 3301.

In some cases, the query coordinator 3304 can allocate more worker nodes3306 or processors 3406 than is supported by the external data sourceand/or submit multiple subqueries to the external data source. Forexample, if the external data source only supports a single partition,the query coordinator 3304 can allocate multiple worker nodes 3306 orprocessors 3406 to send different subqueries to the external data sourceand receive the results back. In this way, the query coordinator 3304can increase the number of parallel reads from the external data source.As a non-limiting example, suppose an external data source only supportsone partition and the query indicates that a data based on an age rangeof 20-49 is to be obtained from the external data source. The querycoordinator can break up the age range into three sets (20-29, 30-39,40-49) and send (or have nodes send) a subquery for each set to theexternal data source. The external data source can process the requestsconcurrently and return results, and may not know that the requests arecoming from the same system 3301. In this way, the system 3301 canreceive results in parallel from an external data source that supports asingle partition. The query coordinator 3304 can similarly send multiplesubqueries to one partition of a multi-partition-supporting externaldata source to increase the parallel reads from the external datasource.

At block 4210, the query coordinator 3304 executes the query asdescribed in greater detail above with reference to block 4010 of FIG.40 . It will be understood that fewer, more, or different blocks can beused as part of the routine 4200. For example, in some embodiments, theroutine 4200 can further include, monitoring nodes during queryexecution, allocating/deallocating resources based on the query, etc. Asanother example, in certain embodiments, the identification of theexternal data source, generation of a subquery and resource allocationcan form part of a processing query block, similar to the process queryblock 3804 of FIG. 38 .

Furthermore, it will be understood that the various blocks describedherein with reference to FIG. 42 can be implemented in a variety oforders. In some cases, the system 3301 can implement some blocksconcurrently or change the order as desired. For example, the system3301 can generate a subquery for the external data source 4206 andallocate resources concurrently 4208 or in any order, as desired.

18.0. Dataset Destination Flow

FIG. 43 is a flow diagram illustrative of an embodiment of a routine4300 implemented by the query coordinator 3304 to execute a query basedon a dataset destination. Although described as being implemented by thequery coordinator 3304, it will be understood that one or more elementsoutlined for routine 4300 can be implemented by one or more computingdevices/components that are associated with the system 3301, such as thesearch head 210, search process master 3302, indexer 206, and/or workernodes 3306. Thus, the following illustrative embodiment should not beconstrued as limiting.

At block 4302, the query coordinator 3304 receives a query, as describedin greater detail above with reference to block 3802 of FIG. 38 . Atblock 4304, the query coordinator identifies the dataset destination, asdescribed in greater detail above with reference to block 3902 of FIG.39 . In some embodiments, the dataset destination can refer to thelocation where query results or partial query results are to be storedby the system 3301. For example, the nodes 3306 can process data fromany dataset source and then store the data in a dataset destination, aswell as provide the results to a client device 404. In some cases, thedataset destination can be the same as the dataset source. For example,data can be read from the ingested data buffer, processed, and thenstored back in the ingested data buffer. However, in certain cases, thedataset destination and dataset source are different. For example, insome embodiments, data is read from the common storage, processed by thenodes, and the results stored in the query acceleration data store 3308,an external data source 3318, an ingested data buffer, etc.

At block 4306, the query coordinator 3304 determines the functionalityof the dataset destination. As described herein with reference to interalia block 3904 of FIG. 39 , each dataset destination, like datasetsources, can have different functionality and capabilities. Thisfunctionality can correspond to how to communicate with the datasetdestination (e.g., the number of partitions supported by the datasetdestination, the APIs, language, or communication protocols of thedataset destination), processing supported by the dataset destination(e.g., commands supported by the dataset destination), etc.

At block 4308, the query coordinator 3304 allocates or assignsresources, such as, but not limited to, worker nodes 3306 or processors3406 to process and communicate results to the dataset destination.Similar to allocating resources to receive data from a dataset source,the query coordinator 3304 can allocate resources to process andcommunicate data to a dataset destination. For example, the querycoordinator 3304 can allocate worker nodes 3306 or processors 3406 basedon the partitions supported by the dataset destination, the processingcapabilities of the dataset destination, etc. As part of allocatingworker nodes 3306 or processors 3406, the query coordinator 3304 caninstruct the worker nodes 3306 or processors 3406 on how to communicatethe data to the dataset destination, include translated commands for thedataset destination, etc.

At block 4310, the query coordinator 3304 executes the query asdescribed in greater detail above with reference to block 4010 of FIG.40 . It will be understood that fewer, more, or different blocks can beused as part of the routine 4300. For example, in some embodiments, theroutine 4300 can further include, monitoring nodes during queryexecution, allocating/deallocating resources based on the query, etc. Asanother example, in certain embodiments, the identification of thedataset destination, determination of dataset destination functionality,and resource allocation can form part of a processing query block,similar to the process query block 3804 of FIG. 38 .

Furthermore, it will be understood that the various blocks describedherein with reference to FIG. 43 can be implemented in a variety oforders. In some cases, the system 3301 can implement some blocksconcurrently or change the order as desired. For example, the system3301 can determine dataset destination functionality 4306 and allocateresources 4308 concurrently or in any order, as desired.

19.0. Serialization and Deserialization Flow

FIG. 44 is a flow diagram illustrative of an embodiment of a routine4400 implemented by a serialization module, of a component of the dataintake and query system 3301 to serialize data for communication to adestination, similar to the serialization/deserialization module 3412 ofFIG. 34 . The destination can be another component of the data intakeand query system 3301 or external to the data intake and query system3301. Although described as being implemented by serialization module,it will be understood that one or more elements outlined for routine4300 can be implemented by one or more computing devices/components thatare associated with the system 3301, such as the search head 210, searchprocess master 3302, indexer 206, and/or worker nodes 3306. Thus, thefollowing illustrative embodiment should not be construed as limiting.

At block 4402, the serialization module identifies events forserialization. In some cases, as part of identifying the events forserialization, the serialization module groups the events. In someembodiments, the serialization module identifies the events forserialization based on a common source or sourcetype of the events, orother shared attribute, or based on a destination for the events. Incertain embodiments, the serialization module identifies events forserialization based on timing information. For example, theserialization module can serialize events received within a certain timeperiod, such as one second, ten second, one minute, etc.

At block 4404, the serialization module determines header informationfor the events. The header information can include the number of eventsin a group, the field names for the events in the group, etc. In somecases, the field names in the header can include all field names acrossall events. For example, if some events have different field names, bothcan be included in the header information. In some cases, the headerinformation can also include mapping information for mapping field namesto field positions (e.g., where a particular field name is locatedwithin an event, etc.). In some embodiments, as part of determining theheader information for the events, the serialization module canserialize the header information. For example, if some field names arerepetitive or have been identified before in previous groups, they canbe replaced with an identifier indicating a cache entry that has thatfield name. The identifier can be used by the receiving component todeserialize the data. Furthermore, the serialization module can updatethe cache based on the header information. For example, if some of theheader information had not been seen before, the serialization modulecan update the cache so that an identifier can be used in place of theheader information in the future.

At block 4406, the serialization module serializes the events. As partof serializing the events, the serialization module can identify fieldvalues in the events and determine whether the field values in eachevent are stored in cache. The field values that are stored in cache canbe replaced with cache identifiers. In addition, the serializationmodule can identify data other data for removal. For example, in someembodiments, certain delimiters, such as ‘,’ or ‘\n’ can be removed fromthe events.

Further, as part of serializing the events, the serialization module canupdate the cache or generate update cache commands for the receiver.Updating the cache can include adding entries for data encountered inthe events or removing entries that have not been used recently. Thecache can be updated with each event or each group and can be performedprior to, after, or concurrently with an event. For example, uponreceiving a group of events, the receiver can update the cache and thenprocess the events, update the cache while processing the events, orupdate the cache after the events are processed. In some cases, thereceiver updates the cache following each event. In some cases, newentries are added to the cache prior to processing the events andentries are removed from the cache after processing the events in agroup.

At 4408, the serialization module communicates the serialized events tothe destination. In some cases, the serialization module communicatesthe events in a streaming fashion. In such embodiments, theserialization module communicates the events once the serializationprocess for that event is completed. In certain embodiments, theserialization module communicates the events as a group. In suchembodiments, the serialization module waits until the group of events isserialized before transmitting the events as a group.

As part of generating the group and serializing the data, theserialization/deserialization module 3412 can determine the number ofevents to group, determine the order and field names for the fields inthe events of the group, parse the events, determine the number offields for each event, identify and serialize serializable field valuesin the event fields, and identify cache deltas. In some cases, theserialization/deserialization module 3412 performs the various tasks ina single pass of the data, meaning that it performs the identification,parsing, and serializing during a single review of the data. In thismanner, the serialization/deserialization module 3412 can operate onstreaming data and avoid adding delay to theserialization/deserialization process.

It will be understood that fewer, more, or different blocks can be usedas part of the routine 4400. For example, in some embodiments, theroutine 4400 can further include, building and updating the cache at thereceiver, etc. Furthermore, it will be understood that the variousblocks described herein with reference to FIG. 44 can be implemented ina variety of orders. In some cases, the serialization module canimplement some blocks concurrently or change the order as desired. Forexample, the serialization module can determine header information 4404and serialize the events 4406 concurrently or in any order, as desired.Furthermore, although not explicitly described herein, it will beunderstood that the data can be deserialized in a similar manner. Thatis, the receiver can determine the number of events in the group and thefields based on the header information and deserialize each event usingthe cache and data in the serialized group.

20.0. Accelerated Query Results Flow

FIG. 45 is a flow diagram illustrative of an embodiment of a routine4500 implemented by the query coordinator 3304 to execute a queryutilizing a data store (e.g., query acceleration data store 3308).Although described as being implemented by the query coordinator 3304,it will be understood that one or more elements outlined for routine4500 can be implemented by one or more computing devices/components thatare associated with the system 3301, such as the search head 210, searchprocess master 3302, indexer 206, and/or worker nodes 3306. Thus, thefollowing illustrative embodiment should not be construed as limiting.

At block 4502, the query coordinator 3304 receives a query, as describedin greater detail above with reference to block 3802 of FIG. 38 . In theexample of FIG. 45 , the query can reference a particular dataset storedby the query acceleration data store 3308, and reference informationwhich is to be obtained from another dataset source (e.g., external datasource 3318, ingested data buffer, common storage, indexers 206, etc.).

At block 4504, first partial results are identified. As described above,a query can indicate datasets, including a particular dataset that isstored in the query acceleration data store 3308. The query accelerationdata store 3308 can store datasets that are indicated (e.g., by users,for example based on the users including a particular command) asbenefiting from storage in the query acceleration data store 3308 (e.g.,benefitting from caching). In addition, the datasets stored in the queryacceleration data store 3308 can correspond to results or partialresults of queries previously processed by the system 3301. The querycoordinator 3304 can determine that the received query references one ormore datasets stored by the query acceleration data store. For example,the query may specify a dataset is stored in the query acceleration datastore 3308 and/or provide a unique identifier associated with a storeddataset, and the system 3301 (e.g., the query coordinator 3304) mayrelay this unique identifier to the worker nodes 3306 to obtain thereferenced dataset(s). In certain cases, the system 3301 can prompt theuser with identifiers of datasets stored in the query acceleration datastore 3308.

In some cases, the query coordinator 3304 can intelligently determinethat a portion of the data identified for processing in the querycorresponds to data that was previously processed. For example, thequery coordinator 3304 can compare the query with previous queries. Thecomparison can be made against all queries received by the system orqueries received by the system from a particular user or group of users.As yet another example, suppose a query indicates that the last sixtyminutes of data from a particular dataset source is to be processed. Thequery coordinator 3304 can compare the query with previous queries fromthe user and determine that a similar query was received thirty minutespreviously indicating that the prior thirty minutes of data from thedataset source was to be processed and the results of the query storedin the query acceleration data store 3308. Based on that information,the query coordinator 3304 can determine that the first thirty minutesof the sixty minutes' worth of data has already been processed and theresults are accessible in the query acceleration data store 3308.

As described above, worker nodes 3306 can utilize the particular datasetobtained from the data store to determine results. Since the queryacceleration data store 3308 stores the particular dataset, firstpartial results can be rapidly identified by the worker nodes 3306, andthe query coordinator 3304 can provide the first partial results to arequesting client. For example, the first partial results may beminimally processed data that was previously obtained from anotherdataset source (e.g., an external data source 3318, indexers 206,ingested data buffer) and stored in the query acceleration data store3308 with little or no processing by the worker nodes 3306. For example,the worker nodes 3306 may have imported the data from an external datasource 3318 and stored the received data as-is in the query accelerationdata store 3308. The imported results can correspond to raw machine dataor processed data.

Additionally, the first partial results can correspond to results orpartial results of a previous query that were obtained after datareceived by a dataset source was processed the worker nodes 3306. Forexample, the worker nodes 3306 may have imported the data from anexternal data source 3318, ingested data buffer, indexers 206, or evendata stored in the query acceleration data store 3308, performed one ormore transformations on the data, (e.g., extracted relevant portions,combined the data with results from other dataset sources, etc.), andthen stored the results of the processing in the query acceleration datastore 3308.

At block 4506, the query coordinator 3304 dynamically allocatesresources, such as, but not limited to, partitions, worker nodes 3306,or processors 3406. The resources can be allocated to receive andprocess data from a dataset source referenced in the received query(second portion of the set of data), combine results of processing thedata from the dataset source (second partial results) with the firstpartial results, process the combined results, and communicate theresults to a destination, such as the query coordinator 3304, searchhead 210, client device 404, or a dataset destination. As described inblock 4504, the query can indicate a particular dataset stored in thequery acceleration data store 3308. Additionally, the query can furtherindicate that data is to be obtained from another dataset source,processed, and the second partial results combined with the firstpartial results. The query coordinator 3304 can allocate resources basedon the query requirements and the data received from the dataset sourceas described herein. In some cases, the query does not indicate that thefirst partial results are stored in the query acceleration data store3308. In such embodiments, the query can identify a dataset source forobtaining data and the query coordinator 3304 can analyze the query todetermine that a first portion of the data requested corresponds to thefirst partial results stored in the query acceleration data store 3308.

In some embodiments, the dynamic resource allocation can includeallocating resources to receive and process the first partial resultsfrom the query acceleration data store 3308. In addition, in some cases,the query coordinator 3304 can allocate resources to store the secondpartial results or combined results in the accelerated data store 3308for later use, similar to the first partial results.

At block 4508, the query coordinator 3304 executes the query asdescribed in greater detail above with reference to block 4010 of FIG.40 . It will be understood that fewer, more, or different blocks can beused as part of the routine 4500. For example, in some embodiments, theroutine 4500 can further include, monitoring nodes during queryexecution, allocating/deallocating resources based on the query, etc. Asanother example, in certain embodiments, identification of the firstpartial results and resource allocation can form part of a processingquery block, similar to the process query block 3804 of FIG. 38 .Further, the first partial results can be communicated to the clientas-is or further processed by the worker nodes 3306 (e.g., logic can beapplied to the first partial results), and then provided to therequesting client.

Furthermore, it will be understood that the various blocks describedherein with reference to FIG. 45 can be implemented in a variety oforders. In some cases, the system 3301 can implement some blocksconcurrently or change the order as desired. For example, the querycoordinator 3304 can identify the first partial results 4504 andallocate resources 4506 concurrently or in any order, as desired. Duringexecution, the nodes can concurrently obtain the first partial resultsfrom the query acceleration data store 3308 and obtain and process otherdata from another dataset source, or concurrently provide the firstpartial results to the query coordinator 3304 or client device 404 andobtain and process other data from another dataset source, etc.

21.0. Common Storage Architecture

As discussed above, indexers 206 may in some embodiments operate both toingest information into a data intake and query system 3301, and tosearch that information in response to queries from client devices 404.The use of an indexer 206 to both ingest and search information may bebeneficial, for example, because indexers 206 may have ready access toinformation that they have ingested, and thus be enabled to quicklyaccess that information for searching purposes. However, use of anindexer 206 to both ingest and search information may not be desirablein all instances. As an illustrative example, consider an instance inwhich information within the system 3301 is organized into buckets, andeach indexer 206 is responsible for maintaining buckets within a datastore 208 corresponding to the indexer 206. Illustratively, a set of 10indexers 206 may maintain 100 buckets, distributed evenly across tendata stores 208 (each of which is managed by a corresponding indexer206). Information may be distributed throughout the buckets according toa load-balancing mechanism used to distribute information to theindexers 206 during data ingestion. In an idealized scenario,information responsive to a query would be spread across the 100buckets, such that each indexer 206 may search their corresponding 10buckets in parallel, and provide search results to a search head 360.However, it is expected that this idealized scenario may not alwaysoccur, and that there will be at least some instances in whichinformation responsive to a query is unevenly distributed across datastores 208. As an extreme example, consider a query in which responsiveinformation exists within 10 buckets, all of which are included in asingle data store 208 associated with a single indexer 206. In such aninstance, a bottleneck may be created at the single indexer 206, and theeffects of parallelized searching across the indexers 206 may beminimal. To increase the speed of operation of search queries in suchcases, it may therefore be desirable to configure the data intake andquery system 3301 such that parallelized searching of buckets may occurindependently of the operation of indexers 206.

Another potential disadvantage in utilizing an indexer 206 to bothingest and search data is that computing resources of the indexers 206may be split among those two tasks. Thus, ingestion speed may decreaseas resources are used to search data, or vice versa. It may further bedesirable to separate ingestion and search functionality, such thatcomputing resources available to either task may be scaled ordistributed independently.

One example of a configuration of the data intake and query system 3301that enables parallelized searching of buckets independently of theoperation of indexers 206 is shown in FIG. 46 . The embodiment of system3301 that is shown in FIG. 46 substantially corresponds to theembodiment of the system 3301 as shown in FIG. 33 , and thus,corresponding elements of the system 3301 will not be re-described.However, unlike the embodiment as shown in FIG. 33 , where individualindexers 206 are assigned to maintain individual data stores 208, theembodiment of FIG. 46 includes a common storage 4602. Common storage4602 may correspond to any data storage system accessible to each of theindexers 206. For example, common storage 4602 may correspond to astorage area network (SAN), network attached storage (NAS), othernetwork-accessible storage system (e.g., a ho33sted storage system,which may also be referred to as “cloud” storage), or combinationthereof. The common storage 4602 may include, for example, hard diskdrives (HDDs), solid state storage devices (SSDs), or othersubstantially persistent or non-transitory media. Data stores 208 withincommon storage 4602 may correspond to physical data storage devices(e.g., an individual HDD) or a logical storage device, such as agrouping of physical data storage devices or a virtualized storagedevice hosted by an underlying physical storage device. In oneembodiment, common storage 4602 may be multi-tiered, with each tierproviding more rapid access to information stored in that tier. Forexample, a first tier of the common storage 4602 may be physicallyco-located with indexers 206 and provide rapid access to information ofthe first tier, while a second tier may be located in a differentphysical location (e.g., in a hosted or “cloud” computing environment)and provide less rapid access to information of the second tier.Distribution of data between tiers may be controlled by any number ofalgorithms or mechanisms. In one embodiment, a first tier may includedata generated or including timestamps within a threshold period of time(e.g., the past seven days), while a second tier or subsequent tiersincludes data older than that time period. In another embodiment, afirst tier may include a threshold amount (e.g., n terabytes) orrecently accessed data, while a second tier stores the remaining lessrecently accessed data. In one embodiment, data within the data stores208 is grouped into buckets, each of which is commonly accessible to theindexers 206. The size of each bucket may be selected according to thecomputational resources of the common storage 4602 or the data intakeand query system 3301 overall. For example, the size of each bucket maybe selected to enable an individual bucket to be relatively quicklytransmitted via a network, without introducing excessive additional datastorage requirements due to metadata or other overhead associated withan individual bucket. In one embodiment, each bucket is 750 megabytes insize.

The indexers 206 may operate to communicate with common storage 4602 andto generate buckets during ingestion of data. Data ingestion may besimilar to operations described above. For example, information may beprovided to the indexers 206 by forwarders 204, after which theinformation is processed and stored into buckets. However, unlike someembodiments described above, the buckets may be stored in common storage4602, rather than in a data store 208 maintained by an individualindexer 206. Thus, the common storage 4602 can render information of thedata intake and query system 3301 commonly accessible to elements ofthat system 3301. As will be described below, such common storage 4602can beneficial enable parallelized searching of buckets to occurindependently of the operation of indexers 206.

As noted above, it may be beneficial in some instances to separatewithin the data intake and query system 3301 functionalities ofingesting data and searching for data. As such, in the illustrativeconfiguration of FIG. 46 , worker nodes 3306 may be enabled to searchfor data stored within common storage 4602. The nodes 3306 may thereforebe communicatively attached (e.g., via a communication network) with thecommon storage 4602, and be enabled to access buckets within the commonstorage 4602. The nodes 3306 may search for data within buckets in amanner similar to how searching may occur at the indexers 206, asdiscussed in more detail above. However, because nodes 3306 in someinstances are not statically assigned to individual data stores 208 (andthus to buckets within such a data store 208), the buckets searched byan individual node 3306 may be selected dynamically, to increase theparallelization with which the buckets can be searched. For example,using the example provided above, consider again an instance whereinformation is stored within 100 buckets, and a query is received at thedata intake and query system 3301 for information within 10 suchbuckets. Unlike the example above (in which only indexers 206 alreadyassociated with those 10 buckets could be used to conduct a search), the10 buckets holding relevant information may be dynamically distributedacross worker nodes 3306. Thus, if 10 worker nodes 3306 are available toprocess a query, each worker node 3306 may be assigned to retrieve andsearch within 1 bucket, greatly increasing parallelization when comparedto the low-parallelization scenario discussed above (e.g., where asingle indexer 206 is required to search all 10 buckets). Moreover,because searching occurs at the worker nodes 3306 rather than atindexers 206, computing resources can be allocated independently tosearching operations. For example, worker nodes 3306 may be executed bya separate processor or computing device than indexers 206, enablingcomputing resources available to worker nodes 3306 to scaleindependently of resources available to indexers 206.

Operation of the data intake and query system 3301 to utilize workernodes 3306 to search for information within common storage 4602 will nowbe described. As discussed above, a query can be received at the searchhead 210, processed at the search process master 3302, and passed to aquery coordinator 3304 for execution. The query coordinator 3304 maygenerate a DAG corresponding to the query, in order to determinesequences of search phases within the query. The query coordinator 3304may further determine based on the query whether each branch of the DAGrequires searching of data within the common storage 4602 (e.g., asopposed to data within external storage, such as remote systems 414 and416).

It will be assumed for the purposes of described that at least onebranch of the DAG requires searching of data within the common storage4602, and as such, description will be provided for execution of such abranch. While interactions are described for executing a single branchof a DAG, these interactions may be repeated (potentially concurrentlyor in parallel) for each branch of a DAG that requires searching of datawithin the common storage 4602. As discussed above with reference toFIG. 36 , executing a search representing a branch of a DAG can includea number of phases, such as an intake phase 3604, processing phase 3606,and collector phase 3608. It is therefore illustrative to discussexecution of a branch of a DAG that requires searching of the commonstorage 4602 with reference to such phases. As also discussed above,each phase may be carried out using a number of partitions operated onby one or more worker nodes 3306, which can also refer to one or moreprocessors 3406 within a worker node 3306, execution environments withina worker node 3306 or processor 3406 of a worker node 3306, such as avirtualized computing device or software-based container, etc.

When a branch requires searching within common storage 4602, the querycoordinator 3304 can select a worker node 3306 at random or according toa load-balancing algorithm to gather metadata regarding the informationwithin the common storage 4602, for use in dynamically assigningpartitions or worker nodes 3306 to implement an intake phase 3604.Metadata is discussed in more detail above, but may include, forexample, data identifying a host, a source, and a source type related toa bucket of data. Metadata may further indicate a range of timestamps ofinformation within a bucket. The metadata can then be compared against aquery to determine a subset of buckets within the common storage 4602that may contain information relevant to a query. For example, where aquery specifies a desired time range, host, source, source type, orcombination thereof, only buckets in the common storage 4602 thatsatisfy those specified parameters may be considered relevant to thequery. In one embodiment, the subset of buckets is determined by theassigned worker node 3306 and returned to the query coordinator 3304. Inanother embodiment, the metadata retrieved by a worker node 3306 isreturned to the query coordinator 3304 and used by the query coordinator3304 to determine the subset of buckets.

Thereafter, the query coordinator 3304 can dynamically assign workernodes 3306 to intake individual buckets within the determined subset ofbuckets. During execution, the buckets can be assigned to one or morepartitions and processed by the worker nodes 3306 or processors 3406.For example, the contents of a bucket can be assigned to a worker node3306. Based on the size of the contents of the bucket, the worker node3306 can generate one or more partitions from the bucket's contents. Theworker node 3306 can then assign the one or more partitions to aprocessor 3406 for processing.

In one embodiment, the query coordinator 3304 attempts to maximizeparallelization of the intake phase 3604, by attempting to intake thesubset of buckets with a number of worker nodes 3306 or processors 3406equal to the number of buckets in the subset (e.g., resulting in aone-to-one mapping of buckets in the subset to worker nodes 3306 orprocessors 3406). However, such parallelization may not be feasible ordesirable, for example, where the total number of worker nodes 3306 orprocessors 3406 is less than the number of buckets within the determinedsubset, where some worker nodes 3306 or processors 3406 are processingother queries, or where some worker nodes 3306 or processors 3406 shouldbe left in reserve to process other queries. Accordingly, the querycoordinator 3304 may interact with the workload advisor 3310 todetermine a number of partitions, worker nodes 3306, or processors 3406that are to be utilized to conduct the intake phase 3604 of the query.Illustratively, the query coordinator 3304 may initially request aone-to-one correspondence between buckets and worker nodes 3306 orprocessors 3406, and the workload advisor 3310 may reduce the number ofworker nodes 3306 or processors 3406 used for the intake phase 3604 ofthe query, resulting in a 2-to-1, 3-to-1, or n-to-1 correspondencebetween buckets and worker nodes 3306 or processors 3406. Operation ofthe workload advisor 3310 is described in more detail above.

The query coordinator 3304 can then assign the worker nodes 3306 orprocessors 3406 (e.g., those worker nodes 3306 or processors 3406identified by interaction with the workload advisor 3310) to intake thebuckets previously identified as potentially containing relevantinformation (e.g., based on metadata of the buckets). In one embodiment,the query coordinator 3304 may assign all buckets as a single operation.For example, where 10 buckets are to be searched by 5 worker nodes 3306or processors 3406, the query coordinator 3304 may assign 2 buckets to afirst worker node 3306 or processor 3406, two buckets to a second workernode 3306 or processor 3406, etc. In another embodiment, the querycoordinator 3304 may assign buckets iteratively. For example, where 10buckets are to be searched by 5 worker nodes 3306 or processors 3406,the query coordinator 3304 may initially assign five buckets (e.g., onebucket to each worker node 3306 or processor 3406), and assignadditional buckets to each worker node 3306 or processor 3406 as therespective worker node 3306 or processor 3406 completes intake ofpreviously assigned buckets.

In some instances, buckets may be assigned to worker nodes 3306randomly, or in a simple sequence (e.g., a first worker node 3306 isassigned a first bucket, a second worker nodes 3306 is assigned a secondbucket, etc.). In other instances, the query coordinator 3304 may assignbuckets to worker nodes 3306 based on buckets previously assigned toworker nodes 3306, in a prior or current search. Illustratively, in someembodiments each worker node 3306 may be associated with a local cacheof information (e.g., in memory, such as random access memory (“RAM”) ordisk-based cache). Each worker node 3306 may store copies of one or morebuckets from the common storage 4602 within the local cache, such thatthe buckets may be more rapidly searched by the worker node 3306. Thequery coordinator 3304 may maintain or retrieve from worker nodes 3306information identifying, for each relevant node 3306, what buckets arecopied within local cache of the respective nodes 3306. Where a searchnode 3306 assigned to execute a search has within its local cache a copyof a bucket determined to be potentially relevant to the search, thatworker node 3306 may be preferentially assigned to search thatlocally-cached bucket. In some instances, local cache information canfurther be used to determine the worker nodes 3306 to be used to conducta search. For example, worker nodes 3306 that have locally-cached copiesof buckets potentially relevant to a search may be preferentiallyselected by the query coordinator 3304 or workload advisor 3310 toexecute the intake phase 3604 of a search. In some instances, the querycoordinator 3304 or other component of the system 3301 (e.g., the searchprocess master 3302) may instruct worker nodes 3306 to retrieve andlocally cache copies of various buckets from the common storage 4602,independently of processing queries. In one embodiment, the system 3301is configured such that each bucket from the common storage 4602 islocally cached on at least one worker node 3306. In another embodiment,the system 3301 is configured such that at least one bucket from thecommon storage 4602 is locally cached on at least two worker nodes 3306.Caching a bucket on at least two worker nodes 3306 may be beneficial,for example, in instances where different queries both require searchingthe bucket (e.g., because the at least two worker nodes 3306 may processtheir respective local copies in parallel). In still other embodiments,the system 3301 is configured such that all buckets from the commonstorage 4602 are locally cached on at least a given number n of workernodes 3306, wherein n is defined by a replication factor on the system3301. For example, a replication factor of 5 may be established toensure that 5 searches of buckets can be executed concurrently by 5different worker nodes 3306, each of which has locally cached a copy ofa given bucket potentially relevant to the searches.

In some embodiments, buckets may further be assigned to partitions toassist with time ordering of search results. For example, where a searchrequests time ordering of results, the query coordinator 3304 mayattempt to assign buckets with overlapping time ranges to the samepartition, such that information within the buckets can be sorted in thepartition. Where the buckets assigned to different partitions arenon-overlapping in time, the query coordinator 3304 may sort informationfrom different partitions according to an absolute ordering of thebuckets processed by the different worker nodes 3306. That is, if alltimestamps in all buckets processed by a first worker node 3306 occurprior to all timestamps in all buckets processed by a second worker node3306, query coordinator 3304 can quickly determine (e.g., withoutreferencing timestamps of information) that all information identifiedby the first worker node 3306 in response to a search occurs in timeprior to information identified by the second worker node 3306 inresponse to the search. Thus, assigning buckets with overlapping timeranges to the same partition can reduce computing resources needed totime-order results.

In still more embodiments, partitions may be assigned to worker nodes3306 based on overlaps of computing resources. For example, where aworker node 3306 is required to retrieve a bucket from common storage4602 (e.g., where a local cached copy of the bucket does not exist onthe worker node 3306 assigned to the partition), such retrieval may usea relatively high amount of network bandwidth or disk read/writebandwidth on the worker node 3306. Thus, assigning a second bucket thatrequires retrieval to the same worker node 3306 might strain or exceedthe network or disk read/write bandwidth of the worker node 3306. Forthis reason, it may be preferential to assign buckets to partitions suchthat multiple processors 3406 of a common worker node 3306 are not bothrequired to retrieve buckets from the common storage 4602.Illustratively, it may be preferential to evenly assign all bucketscontaining potentially relevant information among the different workernodes 3306 used to implement the intake phase 3604. For similar reasons,where a given worker node 3306 has within its local cache two bucketsthat potentially include relevant information, it may be preferential toassign both such buckets to same worker node 3306, such that bothbuckets can be searched in parallel on the worker node 3306 by therespective processor 3406. In some instances, commonality of computingresources between partitions can further be used to determine the workernodes 3306 to be used to conduct an intake phase 3604. For example, thequery coordinator 3304 may preferentially assign different worker nodes3306 to implement an intake phase 3604 (e.g., in order to maximizenetwork or disk read/write bandwidth). However, where a worker node 3306has locally cached multiple buckets with information potentiallyrelevant to the search, the query coordinator 3304 may preferentiallyassign those buckets to that worker node 3306.

The above mechanisms for assigning buckets to worker nodes may becombined based on priorities of each potential outcome. For example, thequery coordinator 3304 may give an initial priority to distributingbuckets across a maximum number of different worker nodes 3306, but ahigher priority to assigning the same worker node 3306 to processbuckets with overlapping timestamps. The query coordinator 3304 may giveyet a higher priority to assigning worker nodes 3306 to process bucketsthat have been locally cached. The query coordinator 3304 may stillfurther give higher priority to ensuring that each worker node 3306 issearching at least one bucket for information responsive to a query atany given time. Thus, the query coordinator 3304 can dynamically alterthe assignment of buckets to worker nodes 3306 to increase theparallelization of a search, and to increase the speed and efficiencywith which the search is executed.

When searching for information within the common storage 4602, theintake phase 3604 may be carried out according to bucket-to-worker node3306 mapping discussed above, as determined by the query coordinator3304. Specifically, after assigning at least one bucket to each workernode 3306 during the intake phase 3604, each worker node 3306 may beginto retrieve its assigned bucket. Retrieval may include, for example,downloading a corresponding bucket from the common storage 4602, orlocating a copy of the bucket in a local cache of the worker node 3306.Thereafter, each worker node 3306 may conduct an initial search of thebucket for information responsive to a query. The initial search mayinclude processing that is expected to be disk or network intensive,rather than processing (e.g., CPU) intensive. For example, the initialsearch may include accessing the bucket, which may include decompressingthe bucket from a compressed format, and accessing an index file storedwithin the bucket. The initial search may further include referencingthe index or other information (e.g., metadata within the bucket) tolocate one or more portions (e.g., records or individual files) of thebucket that potentially contain information relevant to the search.Moreover, in some embodiments, processing the bucket can includegenerating one or more partitions from the bucket and assigning the oneor more partitions to one or more processors 3406 for processing.

Thereafter, the search proceeds to the processing phase 3606, where theportions of buckets identified during the intake phase 3604 are searchedto locate information responsive to the search. Illustratively, thesearching that occurs during the processing phase 3606 may be predictedto be more processor (e.g., CPU) intensive than that which occurredduring the intake phase 3604. As such, the number of partitions used toconduct the processing phase 3606 may vary from that of the intake phase3604. For example, during or after the conclusion of the intake phase3604, each worker node 3306 implementing that phase 3604 may communicateto the query coordinator 3304 information regarding the portionsidentified as potentially containing information relevant to the query(e.g., the number, size, or formatting of portions, etc.). The querycoordinator 3304 may thereafter determine from that information (e.g.,based on interactions with the workload advisor 3310) the partitions tobe used to conduct the processing phase 3606. In other embodiments, thequery coordinator 3304 may select worker nodes 3306 to be used toconduct the processing phase 3606 prior to implementation of the intakephase 3604 (e.g., contemporaneously with selecting worker nodes 3306 toconduct the intake phase 3604). The worker node 3306 selected forconducting the processing phase 3606 may include one or more worker node3306 that previously conducted the intake phase 3604. However, becausethe processing phase 3606 may be expected to be more resource intensivethan the intake phase 3604 (e.g., with respect to use of processingcycles), the number of partitions used in the processing phase 3606 mayexceed the number of partitions used in the intake phase 3604. To reducenetwork communications, the additional partitions used in the processingphase 3606 may be preferentially selected to be collocated on a workernode 3306 with a partition that was previously used during intake phase3604.

At the processing phase 3606, the worker nodes 3306 may parse theportions of buckets located during the intake phase 3604 in order toidentify information relative to a search. For example, the worker node3306 may parse the portions of buckets (e.g., individual files orrecords) to identify specific lines or segments that contain valuesspecified within the search, such as one or more error types desired tobe located during the search. Where the search is conducted according tomap-reduce techniques, the processing phase 3606 can correspond toimplementing a map function. Where the search requires that results betime-ordered, the processing phase 3606 may further include sortingresults at each partition into a time-ordering.

The remainder of the search may be executed in phases according to theDAG determined by the query coordinator 3304. For example, where thebranch of the DAG currently being processed includes a collection node,the search may proceed to a collector phase 3608. The collector phase3608 may be executed using one or more worker nodes 3306 selected by thequery coordinator 3304 (e.g., based on the information identified duringthe processing phase 3606), and operate to aggregate informationidentified during the processing phase 3606 (e.g., according to a reducefunction). Where the processing phase 3606 represents a top-node of abranch of the DAG being executed, the information located during theprocessing phase 3606 may be transmitted to the query coordinator 3304,where any additional nodes of the DAG are completed, and search resultsare transmitted to a data destination 3616. These additional phases maybe implemented in a similar manner as described above, and they aretherefore not discussed in detail with respect to searches against acommon storage 4602.

As will be appreciated in view of the above description, the use of acommon storage 4602 can provide many advantages within the data intakeand query system 3301. Specifically, use of a common storage 4602 canenable the system 3301 to decouple functionality of data ingestion, asimplemented by indexers 206, with functionality of searching, asimplemented by worker nodes 3306. Moreover, because buckets containingdata are accessible by each worker node 3306, a query coordinator 3304can dynamically allocate buckets to worker nodes 3306 at the time of asearch in order to maximize parallelization. Thus, use of a commonstorage 4602 can substantially improve the speed and efficiency ofoperation of the system 3301.

22.0. Common Storage Flow

FIG. 47 is a flow diagram illustrative of an embodiment of a routine4700 implemented by the query coordinator 3304 to execute a query ondata within common storage 4602. Although described as being implementedby the query coordinator 3304, it will be understood that one or moreelements outlined for routine 4700 can be implemented by one or morecomputing devices/components that are associated with the system 3301,such as the search head 360, search process master 3302, indexer 206,and/or worker nodes 3306. Thus, the following illustrative embodimentshould not be construed as limiting.

At block 4702, the query coordinator 3304 receives a query, as describedin greater detail above with reference to block 3802 of FIG. 38 . Atblock 4704, the query coordinator identifies the common storage 4602 asa data source for the query (e.g., based on parameters of the query,based on timing requirements as described in greater detail above withreference to block 3902 of FIG. 39 , etc.).

At block 4706, the query coordinator 3304 determines one or more bucketswithin the common storage 4602 that may contain potentially relevantinformation for the query. As noted above, the one or more buckets maybe identified based on metadata of the buckets within common storage4602, including time ranges, sources, source types, or hosts related toinformation stored within each bucket. In one embodiment, the querycoordinator 3304 may utilize a worker node 3306 to retrieve currentmetadata of buckets within the common storage 4602, and the querycoordinator 3304 may utilize this information to determine potentiallyrelevant buckets. In another embodiment, the query coordinator 3304 maydirect a worker node 3306 to retrieve current metadata of buckets withinthe common storage 4602 and to utilize this information to determinepotentially relevant buckets, after which the worker node 3306 maynotify the query coordinator 3304 of the potentially relevant buckets.

At block 4708, the query coordinator 3304 allocates resources, such as,but not limited to partitions, worker nodes 3306, or processors 3406, tointake the potentially relevant buckets during an intake phase 3604. Asdescribed above, the query coordinator 3304 can allocate resources basedon a number of factors, including a number of potentially relevantbuckets, amount of memory available, a number of worker nodes 3306 orprocessors 3406 available to intake the buckets, a number of potentiallyrelevant buckets that exist as cached copies within local storage of aworker node 3306, or a distribution of partitions across differentworker nodes 3306 (e.g., to maximize an availability of network or diskread/write bandwidth). In some embodiments, the query coordinator 3304may interact with the workload advisor 3310 to intake potentiallyrelevant buckets. In general, worker nodes 3306 may be allocated tointake potentially relevant buckets in a manner that maximizes either orboth of use of locally-cached copies of buckets on worker nodes 3306 andparallelization of retrieval of buckets from common storage 4602.

At block 4710, the query coordinator 3304 executes the query asdescribed in greater detail above with reference to FIGS. 36 and 46 . Itwill be understood that fewer, more, or different blocks can be used aspart of the routine 4700. For example, in some embodiments, the routine4700 can further include allocating resources to conduct subsequentphases of a query, such as a processing phase 3606 or collection phase3608. As another example, in certain embodiments, the identification ofthe common storage 4602, determination of potentially relevant buckets,and allocation of worker nodes 3306 to perform an intake phase 3604 canform part of a processing query block, similar to the process queryblock 3804 of FIG. 38 .

Furthermore, it will be understood that the various blocks describedherein with reference to FIG. 47 can be implemented in a variety oforders. In some cases, the system 3301 can implement some blocksconcurrently or change the order as desired. For example, the system3301 can in some instances allocate or instruct worker nodes 3306 tointake potentially relevant buckets iteratively, during execution of aquery (e.g., by allocating worker nodes 3306 to a first portion ofpotentially relevant buckets, and allocating worker nodes 3306 toadditional buckets from the potentially relevant buckets as the workernodes 3306 complete intake of buckets from the first portion).

The above interactions generally discuss information that is storedwithin common storage 4602. However, because the information in commonstorage 4602 is generated by indexers 206, searching of common storage4602 may be undesirable in instances in which search results are desiredimmediately. Specifically, where information from a data source 203 isrequired to pass through a forwarder 204, be processed at an indexer206, and stored in common storage 4602 before searching of thatinformation can be conducted by a worker node 3306, a significant delay(e.g., 2-4 minutes) may occur between generation of the information atthe data source 203 and searching of the information by a worker node3306. Thus, in the architecture of FIG. 46 , the indexers 206 may beconfigured to enable searching of information received at an indexer 206(prior to processing of that information and storage in the commonstorage 4602), in a manner similar to that described above withreference to FIG. 39 . However, utilization of the indexers 206 toconduct searching of not-yet-indexed information may incur some of thedisadvantages described above, such as the comingling of computingresources used to ingest information with resources used to searchinformation. It may therefore be desirable to provide an architecturethat enables worker nodes 3306, rather than indexers 206, to searchnot-yet-indexed information, without inhibiting operation of theindexers 206.

23.0. Ingested Data Buffer Architecture

One embodiment of the system 3301 that enables worker nodes 3306 tosearch not-yet-indexed information is shown in FIG. 48 . Searching ofnot-yet-indexed information (e.g., prior to processing of theinformation by an indexer 206) may be beneficial, for example, whereinformation is desired on a continuous or streaming basis. For example,a client device 404 a may desire to establish a long-running (e.g.,until manually halted) search of data received at the data intake andquery system 3301, such that the client is quickly notified onoccurrence of specific types of information within the data, such aserrors within machine records. Thus, it may be desirable to conduct thesearch against the data as it enters intake and query system 3301,rather than waiting for the data to be processed by the indexers 206 andsaved into a data store 208.

The embodiment of FIG. 48 is similar to that of FIG. 46 , andcorresponding elements will not be re-described. However, unlike theembodiment of FIG. 46 , the embodiment of FIG. 48 includes an ingesteddata buffer 4802. The ingested data buffer 4802 of FIG. 48 operates toreceive information obtained by the forwarders 204 from the data sources203, and make such information available for searching to both indexers206 and worker nodes 3306. As such, the ingested data buffer 4802 mayrepresent a computing device or computing system in communication withboth the indexers 206 and the worker nodes 3306 via a communicationnetwork.

In one embodiment, the ingested data buffer 4802 operates according to apublish-subscribe (“pub-sub”) messaging model. For example, each datasource 203 may be represented as one or more “topics” within a pub-submodel, and new information at the data source may be represented as a“message” within the pub-sub model. Elements of the system 3301,including indexers 206 and worker nodes 3306 (or processors 3406 ofworker nodes 3306) may subscribe to a topic representing desiredinformation (e.g., information of a particular data source 203) toreceive messages within the topic. Thus, an element subscribed to arelevant topic will be notified of new data categorized under the topicwithin the ingested data buffer 4802. A variety of implementations ofthe pub-sub messaging model are known in the art, and may be usablewithin the ingested data buffer 4802. As will be appreciated based onthe description below, use of a pub-sub messaging model can provide manybenefits to the system 3301, including the ability to search dataquickly after the data is received at the ingested data buffer 4802(relative to waiting of the data to be processed by an indexer 206)while maintaining or increasing data resiliency.

In embodiments that utilize an ingested data buffer 4802, operation ofthe indexer 206 may be modified to receive information from the buffer4802. Specifically, each indexer 206 may be configured to subscribe toone or more topics on the ingested data buffer 4802 and to thereafterprocess the information in a manner similarly to as described above withrespect to other embodiments of the system. After data representing amessage has been processed by an indexer 206, the indexer 206 can sendan acknowledgement of the message to the ingested data buffer 4802. Inaccordance with the pub-sub messaging model, the ingested data buffer4802 can delete a message once acknowledgements have been received fromall subscribers (which may include, for example, a single indexer 206configured to process the message). Thereafter, operation of the system3301 to store the information processed by the indexer 206 and enablesearching of such information is similar to embodiments described above(e.g., with reference to FIGS. 33 and 46 , etc.).

As discussed above, the ingested data buffer 4802 is also incommunication with the worker nodes 3306. As such, the data intake andquery system 3301 can be configured to utilize the worker nodes 3306 tosearch data from the ingested data buffer 4802 directly, rather thanwaiting for the data to be processed by the indexers 206. As discussedabove, a query can be received at the search head 360, processed at thesearch process master 3302, and passed to a query coordinator 3304 forexecution. The query coordinator 3304 may generate a DAG correspondingto the query, in order to determine sequences of search phases withinthe query. The query coordinator 3304 may further determine based on thequery whether any branch of the DAG requires searching of data withinthe ingested data buffer 4802. For example, the query coordinator 3304may determine that at least one branch of the query requires searchingof data within the ingested data buffer 4802 by identifying, within thequery, a topic of the ingested data buffer 4802 for searching. It willbe assumed for the purposes of described that at least one branch of theDAG requires searching of data within the ingested data buffer 4802, andas such, description will be provided for execution of such a branch.While interactions are described for executing a single branch of a DAG,these interactions may be repeated (potentially concurrently or inparallel) for each branch of a DAG that requires searching of datawithin the ingested data buffer 4802. As discussed above with referenceto FIG. 36 , executing a search representing a branch of a DAG caninclude a number of phases, such as an intake phase 3604, processingphase 3606, and collector phase 3608. It is therefore illustrative todiscuss execution of a branch of a DAG that requires searching of thecommon storage 4602 with reference to such phases. As also discussedabove, each phase may be carried out using a number of partitions, eachof which may be assigned to a worker node 3306 (e.g., a specific workernode 3306, processor within the worker node 3306, execution environmentwithin a worker node 3306, etc.). Particularly in the case of streamingor continuous searching, different instances of the phases may becarried out at least partly concurrently. For example, the processingphase 3606 may occur with respect to a first set of information whilethe intake phase 3604 occurs with respect to a second set ofinformation, etc. Thus, while the phases will be discussed in sequencebelow, it should be appreciated that this sequence can occur multipletimes with respect to a single query (e.g., as new data enters thesystem 3301), and each sequence may occur at least partiallyconcurrently with one or more other sequences. Moreover, because theingested data buffer 4802 can be configured to make messages availableto any number of subscribers, the sequence discussed below may occurwith respect to multiple different searches, potentially concurrently.Thus, the architecture of FIG. 48 provides a highly scalable, highlyresilient, high availability architecture for searching informationreceived at the system 3301.

When a branch requires searching within ingested data buffer 4802, thequery coordinator 3304 can select a worker node 3306 at random oraccording to a load-balancing algorithm to gather metadata regarding thetopic specified within the query from the ingested data buffer 4802.Metadata regarding a topic may include, for example, a number of messagequeues within the ingested data buffer 4802 corresponding to the topic.Each message queue can represent a collection of messages published tothe topic, which may be time-ordered (e.g., according to a time that themessage was received at the ingested data buffer 4802). In someinstances, the ingested data buffer 4802 may implement a single messagequeue for a topic. In other instances, the ingested data buffer 4802 mayimplement multiple message queues (e.g., across multiple computingdevices) to aid in load-balancing operation of the ingested data buffer4802 with respect to the topic. The selected worker node 3306 candetermine the number of message queues maintained at the ingested databuffer 4802 for a topic, and return this information to the querycoordinator.

Thereafter, the query coordinator 3304 can dynamically assign workernodes 3306 to an intake phase 3604 by retrieving individual messagequeues of the topic within the ingested data buffer 4802. In oneembodiment, the query coordinator 3304 attempts to maximizeparallelization of the intake phase 3604, by attempting to retrievemessages from the message queues with a number of worker nodes 3306 orprocessors 3406 equal to or greater than the number of message queuesfor the topic maintained at the ingested data buffer 4802 (e.g.,resulting in a one-to-one mapping of message queues in the topic toworker nodes 3306 or processors 3406). However, such parallelization maynot be feasible or desirable, for example, where the total number ofworker nodes 3306 or processors 3406 is less than the number of messagequeues, where some worker nodes 3306 or processors 3406 are processingother queries, or where some worker nodes 3306 or processors 3406 shouldbe left in reserve to process other queries. Accordingly, the querycoordinator 3304 may interact with the workload advisor 3310 todetermine a number of worker nodes 3306 or processors 3406 that are tobe utilized to intake messages from the message queues during the intakephase 3604. Illustratively, the query coordinator 3304 may initiallyrequest a one-to-one correspondence between message queues and workernodes 3306 or processors 3406, and the workload advisor 3310 may reducethe number of worker nodes 3306 or processors 3406 used to read themessage queues, resulting in a 2-to-1, 3-to-1, or n-to-1 correspondencebetween message queues and worker nodes 3306 or processors 3406.Operation of the workload advisor 3310 is described in more detailabove. When a greater than 1-to-1 correspondence exists between queuesand worker nodes 3306 or processors 3406 (e.g., 2-to-1, 3-to-1, etc.),the message queues may be evenly assigned among different worker nodes3306 used to implement the intake phase 3604, to maximize network orread/write bandwidth available to partitions conducting the intake phase3604.

During the intake phase 3604, each worker node 3306 or processor 3406used during the intake phase 3604 can subscribe to those message queuesassigned to the worker node 3306 or processor 3406. Illustratively,where worker node 3306 or processor 3406 are assigned in a 1-to-1correspondence with message queues for a topic in the ingested databuffer 4802, each worker node 3306 or processor 3406 may subscribe toone corresponding message queue. Thereafter, in accordance with thepub-sub messaging model, the worker node 3306 or processor 3406 canreceive from the ingested data buffer 4802 messages publishes withinthose respective message queues. However, to ensure message resiliency,a worker node 3306 or processor 3406 may decline to acknowledge themessages until such messages have been fully searched, and results ofthe search have been provided to a data destination (as will bedescribed in more detail below).

In some embodiments, a worker node 3306 or processor 3406 may, duringthe intake phase 3604 act as an aggregator of messages published to arespective message queue of the ingested data buffer 4802, to define acollection of data as a partition to be processed during an instance ofthe processing phase 3606. For example, the worker node 3306 orprocessor 3406 may collect messages corresponding to a given time-window(such as a 30 second time window, 1 minute time window, etc.), andbundle the messages together as a partition for further processingduring a processing phase 3606 of the search. In one instance, the timewindow may be set to a duration lower than a typical delay needed for anindexer 206 to process information from the ingested data buffer 4802and place the processed information into a data store 208 (as, if atime-window greater than this delay were used, a search could instead beconducted against the data stores 208). The time window may further beset based on an expected variance between timestamps in receivedinformation and the time at which the information is received at theingested data buffer 4802. For example, it is possible the informationarrives at the ingested data buffer 4802 in an out-of-order manner(e.g., such that information with a later timestamp is received prior toinformation with an earlier timestamp). If the actual delay in receivingout-of-order information (e.g., the delay between when information isactually received and when it should have been received to maintainproper time-ordering) exceeds the time window, it is possible that thedelayed information will be processed during a later instance of theprocessing phase 3606 (e.g., with a subsequent bundle of messages), andas such, results derived from the delayed information may be deliveredout-of-order to a data destination. Thus, a longer time-window canassist in maintaining order of search results. In some instances, theingested data buffer 4802 may guarantee time ordering of results withineach message queue (though potentially not across message queues), andthus, modification of a time window in order to maintain ordering ofresults may not be required. In still more embodiments, the time-windowmay further be set based on computing resources available at the workernodes 3306. For example, a longer time window may reduce computingresources used by a worker node 3306 or processor 3406 by enabling alarger collection of messages to be processed as a single partition inthe processing phase 3606. However, the longer time window may increasethe size of the partition and/or delay how quickly an initial set ofresults are delivered to a data destination. Thus, the specifictime-window may vary across embodiments of the present disclosure.

While embodiments are described herein with reference to a collection ofmessages or a partition defined according to a time-window, otherembodiments of the present disclosure may utilize additional oralternative collection techniques. For example, a worker node 3306 orprocessor 3406 may be configured to include no more than a thresholdnumber of messages or a threshold amount of data in a partition orcollection, regardless of a time-window for collection. As anotherexample, a worker node 3306 or processor 3406 may be configured duringthe intake phase 3604 not to aggregate messages, but rather to pass eachmessage to a processing phase 3606 immediately or substantiallyimmediately. Thus, embodiments related to time-windowing of messages areillustrative in nature.

In some embodiments, the worker nodes or processors 3406, during theintake phase 3604 may further conduct coarse filtering on the messagesreceived during a given time-window, in order to identify any messagesnot relevant to a given query. Illustratively, the coarse filtering mayinclude comparison of metadata regarding the message (e.g., a source,source type, or host related to the message), in order to determinewhether the metadata indicates that the message is irrelevant to thequery. If so, such a message may be removed from the collection orpartition prior to the search process proceeding to the processing phase3606. In one embodiment, the coarse filtering does not include searchingfor or processing the actual content of a message, as such processingmay be predicted to be relatively computing resource intensive.

After generating a collection of messages or partition from a respectivemessage queue, the search can proceed to the processing phase 3606,where one or more worker nodes or processors 3406 are utilized to searchthe messages for information relevant to the search query.Illustratively, the searching that occurs during the processing phase3606 may be predicted to be more processor (e.g., CPU) intensive thanthat which occurred during the intake phase 3604. As such, the number ofpartitions used to conduct the processing phase 3606 may vary from thatof the intake phase 3604. For example, during or after the conclusion ofthe intake phase 3604, each partition worker node 3306 implementing thatphase 3604 may communicate to the query coordinator 3304 informationregarding the collections of messages received during a giventime-window (e.g., the number, size, or formatting of messages, etc.).The query coordinator 3304 may thereafter determine from thatinformation (e.g., based on interactions with the workload advisor 3310)the partitions to be used to conduct the processing phase 3606. In otherembodiments, the query coordinator 3304 may select worker nodes 3306 tobe used to conduct the processing phase 3606 prior to implementation ofthe intake phase 3604 (e.g., contemporaneously with selecting workernodes 3306 to conduct the intake phase 3604). The worker nodes 3306selected for conducting the processing phase 3606 may include one ormore worker nodes 3306 that were part of the intake phase 3604. However,because the processing phase 3606 may be expected to be more resourceintensive than the intake phase 3604 (e.g., with respect to use ofprocessing cycles), the number of partitions used in the processingphase 3606 may exceed the number of partitions used in the intake phase3604. To reduce network communications, the additional partitions usedin the processing phase 3606 may be preferentially selected to becollocated on a worker node 3306 with a partition that was used in theintake phase 3604.

At the processing phase 3606, the worker nodes 3306 may parse theportions of buckets located during the intake phase 3604 in order toidentify information relative to a search. For example, the worker node3306 may parse the portions of buckets (e.g., individual files orrecords) to identify specific lines or segments that contain valuesspecified within the search, such as one or more error types desired tobe located during the search. Where the search is conducted according tomap-reduce techniques, the processing phase 3606 can correspond toimplementing a map function. Where the search requires that results betime-ordered, the processing phase 3606 may further include sortingresults at each partition into a time-ordering.

The remainder of the search may be executed in phases according to theDAG determined by the query coordinator 3304. For example, where thebranch of the DAG currently being processed includes a collection node,the search may proceed to a collector phase 3608. The collector phase3608 may be executed using one or more worker nodes 3306 selected by thequery coordinator 3304 (e.g., based on the information identified duringthe processing phase 3606), and operate to aggregate informationidentified during the processing phase 3606 (e.g., according to a reducefunction). Where the processing phase 3606 represents a top-node of abranch of the DAG being executed, the information located during theprocessing phase 3606 may be transmitted to the query coordinator 3304,where any additional nodes of the DAG are completed, and search resultsare transmitted to a data destination 3616. These additional phases maybe implemented in a similar manner as described above, and they aretherefore not discussed in detail with respect to searches against acommon storage 4602.

Subsequent to these phases, a set of search results corresponding toeach collection of messages or partition (e.g., as received during atime-window) may be transmitted to a data destination. On transmissionof such information (and potentially verification of arrival of suchinformation at the data destination), the search head 210 may cause anacknowledgement of each message within the collection to be transmittedto the ingested data buffer 4802. For example, the search head 210 maynotify the query coordinator 3304 that search results for a particularset of information (e.g., information corresponding to a range oftimestamps representing a given time window) have been transmitted to adata destination. The query coordinator 3304 can thereafter notify theworker nodes 3306 used to ingest messages making up the set ofinformation that the search results have been transmitted. The workernodes 3306 can then acknowledge to the ingested data buffer 4802 receiptof the messages. In accordance with the pub-sub messaging model, theingested data buffer 4802 may then delete the messages afteracknowledgement by subscribing parties. By delaying acknowledgement ofmessages until after search results based on such messages aretransmitted to (or acknowledged by) a data destination, resiliency ofsuch search results can be improved or potentially guaranteed. Forexample, in the instance that an error occurs between receiving amessage from the ingested data buffer 4802 and search results based onthat message being passed to a data destination (e.g., a worker node3306 fails, causing a copy of the message maintained at the worker node3306 to be lost), the query coordinator 3304 can detect the failure(e.g., based on heartbeat information from a worker node 3306), andcause the worker node 3306 to be restarted, or a new worker node 3306 toreplace the failed worker node 3306. Because the message has not yetbeen acknowledged to the ingested data buffer 4802, the message isexpected to still exist within a message queue of the ingested databuffer 4802, and thus, the restarted or new worker node 3306 canretrieve and process the message as described below. Thus, by delayingacknowledgement of a message, failures of worker nodes 3306 during theprocess described above can be expected not to result in data losswithin the data intake and query system 3301.

In some embodiments, the ingested data buffer 4802 and searchfunctionalities described above may be used to make “enhanced” orannotated data available for searching in a streaming or continuousmanner. For example, search results may in some instances be representedby codes or other machine-readable information, rather than in aneasy-to-comprehend format (e.g., as error codes, rather than textualdescriptions of what such a code represents). Thus, the embodiment ofFIG. 48 may enable a client to define a long-running search that locatescodes within messages of the ingested data buffer 4802 (e.g., viaregular expression or other pattern matching criteria), correlates thecodes to a corresponding textual description (e.g., via a mapping storedin common storage 4602), annotates or modifies the messages to includerelevant textual descriptions for any code appearing within the message,and re-publishes the messages to the ingested data buffer 4802. In thismanner, the information maintained at the ingested data buffer 4802 maybe readily annotated or transformed by searches executed at the system3301. Any number of types of processing or transformation may be appliedto information of the ingested data buffer 4802 to produce searchresults, and any of such search results may be republished to theingested data buffer 4802, such that the search results are themselvesmade available for searching.

As will be appreciated in view of the above description, the use of aningested data buffer 4802 can provide many advantages within the dataintake and query system 3301. Specifically, use of a ingested databuffer 4802 can enable the system 3301 to utilize worker nodes 3306 tosearch not-yet-indexed information, thus decoupling searching of suchinformation from the functionality of data ingestion, as implemented byindexers 206. Moreover, because the ingested data buffer 4802 can makemessages available to both indexers 206 and worker nodes 3306, searchingof not-yet-indexed information by worker nodes 3306 can be expected notto detrimentally effect the operation of the indexers 206. Stillfurther, because the ingested data buffer 4802 can operate according toa pub-sub messaging model, the system 3301 may utilize selectiveacknowledgement of messages (e.g., after indexing by an indexer 206 andafter delivery of search results based on a message to a datadestination) to increase resiliency of the data on the data intake andquery system 3301. Thus, use of an ingested data buffer 4802 cansubstantially improve the speed, efficiency, and reliability ofoperation of the system 3301.

24.0. Ingested Data Buffer Flow

FIG. 49 is a flow diagram illustrative of an embodiment of a routine4900 implemented by the query coordinator 3304 to execute a query ondata from an ingested data buffer 4802. Although described as beingimplemented by the query coordinator 3304, it will be understood thatone or more elements outlined for routine 4900 can be implemented by oneor more computing devices/components that are associated with the system3301, such as the search head 360, search process master 3302, indexer206, and/or worker nodes 3306. Thus, the following illustrativeembodiment should not be construed as limiting.

At block 4902, the query coordinator 3304 receives a query, as describedin greater detail above with reference to block 3802 of FIG. 38 . Atblock 4904, the query coordinator identifies the ingested data buffer4802 as a data source for the query (e.g., based on parameters of thequery, based on timing requirements as described in greater detail abovewith reference to block 3902 of FIG. 39 , etc.).

At block 4906, the query coordinator 3304 determines a set of messagequeues on the ingested data buffer 4802 to which messages potentiallyrelevant to the query are published. The message queues may bedetermined, for example, by querying the ingested data buffer 4802 basedon a topic specified within the query. In one embodiment, the querycoordinator 3304 may utilize a processor 3406 of a worker node 3306 toretrieve identifying information for the message queues from theingested data buffer 4802. In another embodiment, the query coordinator3304 may directly query the ingested data buffer 4802 for theidentifying information of the message queues.

At block 4908, the query coordinator 3304 allocates worker nodes 3306 toconduct windowed-intake of messages from message queues assigned to theworker nodes 3306. As described above, the query coordinator 3304 canallocate worker nodes 3306 based on a number of factors, including anumber of message queues to which potentially relevant messages areposted, a number of worker nodes 3306 (or processors 3406) available tointake the buckets, or a distribution across different worker nodes 3306(e.g., to maximize an availability of network or disk read/writebandwidth). In some embodiments, the query coordinator 3304 may interactwith the workload advisor 3310 to allocate worker nodes 3306 to intakemessages from message queues. In general, the worker nodes 3306 may beallocated to intake potentially relevant buckets in a manner thatmaximizes parallelization of retrieval of messages from message queueson the ingested data buffer 4802. As noted above, each worker nodes 3306may function to collect messages from its respective message queueduring a given time-window (such as a 30 second time window, 1 minutetime window, etc.) using one or more of its processors 3406, and bundlethe messages together as one or more partitions for further processingduring a processing phase 3606 of the search. The time-window may beselected based on a number of factors, as described in more detailabove.

At block 4910, the query coordinator 3304 executes the query asdescribed in greater detail above with reference to FIGS. 36 and 48 . Itwill be understood that fewer, more, or different blocks can be used aspart of the routine 4700. For example, in some embodiments, the routine4700 can further include allocating worker nodes 3306 to conductsubsequent phases of a query, such as a processing phase 3606 orcollection phase 3608. As another example, in certain embodiments, theidentification of the ingested data buffer 4802, determination ofmessage queues containing potentially relevant messages, and allocationof worker nodes 3306 to perform an intake phase 3604 can form part of aprocessing query block, similar to the process query block 3804 of FIG.38 .

Furthermore, it will be understood that the various blocks describedherein with reference to FIG. 47 can be implemented in a variety oforders. In some cases, the system 3301 can implement some blocksconcurrently or change the order as desired. For example, the system3301 can in some instances allocate worker nodes 3306 to intakepotentially relevant messages from message queues dynamically. Forexample, the system 3301 may periodically or in response to informationreceived from the ingested data buffer 4802 determine that the number ofmessage queues containing potentially relevant messages has changed, andalter the allocation of worker nodes 3306 to those message queuesaccordingly.

25.0. Federated Search

As mentioned above and with reference to FIG. 1A, in some instances itcan be beneficial to perform queries across multiple data systems, suchas the data intake and query system 16 and the external data systems 12.Such queries may result in the correlation of additional data and/or mayprovide additional insights.

In some cases, the external data systems 12 may be distinct deploymentsof a data intake and query system 16 or 108. Specifically, the externaldata systems 12 can include a similar or the same architecture as thedata intake and query system 16 or 108, which may include one or more ofthe previously described systems, such as, for example: forwarders 204,indexers 206, data stores 208, search head 210, search process master3302, query coordinator 3304, worker nodes 3306, accelerated data store3308, common storage 4602, and/or ingested data buffer 4802, etc. Forexample, different divisions of the same company may each use a separateand independent data intake and query system 16 to ingest, store, andsearch their respective datasets. As such, the different and independentdata intake and query systems 16 may have no control over each other orover the data managed by another data intake and query system 16.Furthermore, each deployment of the independent data intake and querysystem 16 can include system-specific search configuration data that maynot be understood by other data intake and query systems 16. Moreover,in some cases, different divisions or subsidiaries of a company may usedifferent versions of a data intake and query system 16, which may eachhave different capabilities of features. For example, a companyimplementing one version of the data intake and query system 16 maypurchase or acquire another company that uses another version of thedata intake and query system 16. In some such cases, the purchasedcompany may remain separately operated or may not have its systemsintegrated with the systems of the purchasing company.

Despite the independent and separate nature of the different data intakeand query systems 16, it can be beneficial for one data intake and querysystem 16 to communicate with and receive and process data from anotherdata intake and query system 16. For example, a user of one data intakeand query system 16 may want to analyze data managed by a different dataintake and query system 16 or correlate data across multiple data intakeand query systems 16. For instance, continuing the example of theprevious paragraph, it may be desirable for an employee of thepurchasing company to request a query that analyzes data from both thedata intake and query system 16 of the purchasing company and the dataintake and query system 16 of the purchased company. As such, one dataintake and query system 16 may receive a query that involves data thatis managed by another data intake and query system 16.

FIG. 50A is a block diagram of an embodiment of the environment 100 inwhich the external data systems 12-1 and 12-2 described with respect toFIG. 1A correspond to data intake and query systems 16B and 16C. Forsimplicity, the data intake and query system that receives a query thatinvolves data managed by another data intake and query system (alsoreferred to herein as a federated query or multi-system query) may bereferred to as the primary data intake and query system 16A, and thedata intake and query systems that perform a query or subquery at therequest of another data intake and query system may be referred to assecondary data intake and query systems 16B, 16C. However, it will beunderstood that any data intake and query system 16A, 16B, 16C(generically referred to as data intake and query system 16) could be aprimary or secondary data intake and query system 16 depending on whichdata intake and query system 16A, 16B, 16C receives the federated queryand which data intake and query system 16A, 16B, 16C executes a query orsubquery at the request of another data intake and query system 16A,16B, 16C. Furthermore, it will be understood that any data intake andquery system 16A, 16B, 16C can include any one or any combination ofcomponents described herein, such as those described with respect to thedata intake and query system 108. Accordingly, the data intake and querysystems 16A, 16B, 16C may each have the same or a different architectureand components.

As will be described herein, upon receipt of a query, a primary dataintake and query system 16A can parse the query and determine that thequery involves one or more secondary data intake and query systems 16B,16C, or is a federated query. The primary data intake and query system16A can communicate with the secondary data intake and query systems16B, 16C to determine the capabilities of each secondary data intake andquery system 16B, 16C and/or estimate the amount of data to be ingestedfrom the secondary data intake and query systems 16B, 16C. In somecases, the primary data intake and query system 16A can obtaininformation regarding search configuration data of the secondary dataintake and query systems 16B, 16C.

Based on the received information, the primary data intake and querysystem 16A can determine the size and number of tasks to be executed inrelation to the query, generate one or more subqueries for eachsecondary data intake and query system 16B, 16C, and/or distribute thesubqueries to the secondary data intake and query systems 16B, 16C forexecution. In certain embodiments, based on the information receivedfrom the secondary data intake and query systems 16B, 16C, the primarydata intake and query system 16A can generate the subquery for differentcomponents of the secondary data intake and query systems 16B, 16C. Forexample, the primary data intake and query system 16A can generate thesubquery for a search head 210 of a secondary data intake and querysystem 16B, 16C and/or for indexers 206 or worker nodes 3306 of thesecondary data intake and query system 16B, 16C.

In certain embodiments, the primary data intake and query system 16Auses the search configuration data received from the secondary dataintake and query system 16B, 16C to generate a native subquery for thesecondary data intake and query system 16B, 16C. In some embodiments, ifthe primary data intake and query system 16A is unable to obtain thesystem-specific search configuration data from the secondary data intakeand query system 16B, 16C, it can generate or use a non-native subqueryfor the secondary data intake and query system 16. In such embodiments,the secondary data intake and query system 16 can process the subqueryto determine the native subquery. However, it will be understood thatthe primary data intake and query system 16A can generate native ornon-native subqueries for different components of the secondary dataintake and query system 16B, 16C as desired.

In some cases, the components of the secondary data intake and querysystems 16B, 16C treat the subqueries similar to other queries that theyreceive. For example, if the subquery is received by a search head 210,the search head 210 can process and execute the query as described ingreater detail herein with reference to at least FIGS. 6, 30, and 38 .Similarly, if a subquery is received by one or more indexers 206 orworker nodes 3306 of the secondary data intake and query system 16B,16C, they can process and execute the queries as described herein.

Further, the secondary data intake and query systems 16B, 16C cancommunicate results of the subqueries (also referred to herein aspartial results or partial results of the federated or multi-systemsearch) to the primary data intake and query system 16A for furtherprocessing. The results of the subqueries can include pre-processed orprocessed data. For example, depending on the capabilities or processingpower of the secondary data intake and query systems 16B, 16C, theprimary data intake and query system 16A can generate subqueries thatpush more or less processing to the secondary data intake and querysystems 16B, 16C.

In embodiments where the primary data intake and query system 16Aincludes worker nodes 3306, the primary data intake and query system 16Acan interact with and receive partial results from the secondary dataintake and query systems 16B, 16C using the worker nodes 3306. Theworker nodes 3306 can concurrently receive and process data receivedfrom one or more secondary data intake and query systems 16B, 16C, andprovide the results to one or more components of the primary data intakeand query system 16A, such as a search head 210, search process master3302, or query coordinator 3304.

In some cases, the subqueries sent to the secondary data intake andquery systems 16B, 16C can indicate that the partial results are to bedistributed among multiple worker nodes 3306. In certain embodiments,the subqueries sent to the secondary data intake and query systems 16B,16C can indicate that the partial results are to be sent to a singleworker node 3306, which can distribute the partial results betweenmultiple worker nodes 3306.

In certain embodiments, the worker nodes 3306 combine the data receivedfrom the secondary data intake and query systems into tasks orpartitions for execution by processors of the worker nodes 3306.Moreover, the worker nodes can distribute the tasks or partitionsbetween worker nodes 3306 in a load-balanced fashion in order to processthe tasks or partitions in a distributed manner.

In some embodiments, the primary and one or more secondary data intakeand query systems 16A, 16B, 16C can include worker nodes 3306. In suchembodiments, each data intake and query system 16A, 16B, 16C canindependently use the worker nodes 3306 to execute their correspondingquery or subquery in a distributed manner.

Further, in some embodiments, one or more worker nodes 3306 may beshared between the primary and one or more secondary data intake andquery systems 16B, 16C. For example, the physical machines on which theworker nodes 3306 are implemented can be communicatively coupled to andreceive instructions from the primary and secondary data intake andquery systems 16B, 16C. Accordingly, in some cases, a secondary dataintake and query system 16B, 16C may use one or more worker nodes 3306to execute a subquery and then provide results of the subquery to theone or more worker nodes 3306 for further execution based on thefederated query. As such, as part of the same query, one or more of theworker nodes 3306 may process data at the direction of a secondary dataintake and query system 16B, 16C and process data at the request of aprimary data intake and query system 16A. Further, the data processed atthe request of the primary data intake and query system 16A cancorrespond to the data processed at the request of the secondary dataintake and query system 16B, 16C. For example, a worker node 3306 mayperform one or more transformations on a first dataset at the request ofthe secondary data intake and query system 16B, 16C and then, at therequest of the primary data intake and query system 16A, perform one ormore transformations on the dataset that resulted from thetransformations on the first dataset.

FIG. 50B is a block diagram of an embodiment of the environment 100 inwhich a primary data intake and query system 16A communicates withthird-party data storage and processing systems 5000A and/or 5000B toexecute a query. The system of FIG. 50B is similar to the system of 50A.For example, the primary data intake and query system 16A is capable ofcommunicating with external data systems 12-1 and 12-2, which mayinclude other data intake and query systems (e.g., the secondary dataintake and query systems 16B and 16C, respectively). However, theprimary data intake and query system 16A of FIG. 50B may also be capableof communicating with external data systems 12-3 and 12-4, which mayinclude third-party data storage and processing systems 5000A and 5000B,respectively (collectively and individually referred to as third-partydata storage and processing system(s) 5000).

The third-party data storage and processing systems 5000 may include anydata storage and processing system that may be designed, created,implemented, published, or otherwise made available from an entity thatdiffers from an entity that designed and/or created the data intake andquery system 16 or 108. Further, the third-party data storage andprocessing systems 5000 may use a different query or command language,or a different interface language than the data intake and query system16. For example, while the data intake and query system may be a SPLUNK®system that is configured to use the Splunk Processing Language (SPL),the third-party data storage and processing systems 5000 may bealternative systems that use alternative languages. For instance, thethird-party data storage and processing systems 5000 may be or mayinclude a system that implements the Elastic Stack® (sometimes referredto as Elasticsearch, Logstash, and Kibana, or the “ELK stack”) and thatuses a query syntax based on the Lucene® query syntax and/or aJSON-based Elasticsearch Query DSL, or a system that implements anOracle® system and that uses a search syntax based on Structured QueryLanguage (SQL). In some embodiments, the third-party data storage andprocessing systems 5000 may differ from each other. For example, thethird-party data storage and processing system 5000A may be an ElasticStack® system and the third-party data storage and processing system5000B may be an Oracle® system.

It should be understood that the number and type of external datasystems 12 are not limited by the example system 100 illustrated in FIG.50B. The system 100 can have any number of external data systems 12 thatcan comprise any number of data intake and query systems 16 incommunication with the primary data intake and query system 16A. In somecases, at least some of the data intake and query system 16 may bedifferent versions of a data intake and query system. For instance, someentities may be using an older or a newer version of the data intake andquery system, or, in some cases, a more or less feature-rich version(e.g., a lite version or a full version) of the data intake and querysystem. Further, the system 100 can have any number of external datasystems 12 that can comprise any type of third-party data storage andprocessing system 5000 that differs in type or version, and/or thatdiffers in the query, command, or programming language used to interactwith the third-party data storage and processing system 5000. Moreover,in some embodiments, at least some of the external data systems 12 maycommunicate with other external data systems in addition to, or insteadof, the primary data intake and query system 16A.

25.1. Federated Search Data Flow

FIG. 51 is a data flow diagram illustrating an embodiment ofcommunications between various components described herein to processand execute a federated or multi-system query. At (1), the search head210 receives and processes a query. At (2), the search head 210communicates the query to the search process service 3702, which canrefer to the search process master 3302 and/or query coordinator 3304.Upon receipt of the query, the search process service 3702 can initiatea query planning or query processing phase 5102 followed by a queryexecution phase 5104.

The query processing phase 5102 can include various steps orcommunications between one or more components of a data intake and querysystem 16A (e.g., search head 210, search process service 3702, querycoordinator 3304, worker nodes 3306, etc.) and external data system(s)12, such as, but not limited to, a secondary data intake and querysystem 16B, 16C or third-party data storage and processing system 5000A5000B, in order to generate query instructions or a query processingscheme.

The query execution phase 5104 can include various steps orcommunications between the primary data intake and query system, workernodes 3306, and external data system(s) 12 as part of executing thequery to provide results to the search head 210. Although illustrated ina particular order, it will be understood that in some cases one or moreportions of the query processing phase 5102 can be performed before,after, or concurrently with one or more portions of the query executionphase 5104 or each other.

As part of the query processing phase 5102 the search process service3702 can (3) parse the query. As described herein, as part of parsingthe query, the query coordinator 3304 can determine that the query to beexecuted is a multi-system query, or involves data managed by anexternal data system 12, such as another data intake and query system 16or a third-party data storage and processing system 5000. In some cases,the query coordinator 3304 can determine that the query to be executedis a multi-system query based on a command, function call, or term inthe query. However, it will be understood that a variety of methods canbe used to indicate that a search is a multi-system query.

In some cases, the query can include details of the subquery for theexternal data systems 12. For example, the query can include a searchstring for the subquery, access information to access the external datasystems 12, and/or other relevant information to enable the primary dataintake and query system to generate a subquery for the external datasystem 12.

As a non-limiting example, in the search below, the term “federated” canindicate that data relevant to the search is located in an external datasystem 12:

 |dfsjob[|union[|search   index=“airline2008”|   stats   count   by   FlightNum][|fromfederated:my_dep_3_search_5]| join usetime=f left=L right=R whereL.FlightNum=R.FlightNum [|union[|search index=“airline2008”| stats countby FlightNum][|from federated:my_dep_2_search_6]|stats count byFlightNum ] | sort -L.FlightNum| head 100]

Thus, according to the above-example, the query includes two non-localdatasets or two subqueries: “my_dep_3_search_5” and “my_dep_2_search_6.”

In certain embodiments, the query can include a reference that can beused to look up or determine the details of the subquery or externaldata system 12. In the above-example, the query includes the references“my_dep_3_search_5” and “my_dep_2_search_6” that can be used to lookupthe details of the subquery using an external query configuration file,directory, or other tool. The external query configuration file caninclude details for the subquery including, but not limited to, syntaxor a string for the subquery that is to be executed on the external datasystems 12, an identifier for the external data systems 12, search type(e.g., streaming, batch, reporting, etc.), maximum or estimate number(or size) of results expected, number of fields used by the subquery orfound in the relevant results, IP address, port number, accesscredentials (e.g., account name/type, password, etc. to access theexternal data system), type of deployment (e.g., secondary data intakeand query system 16, third-party data storage and processing system5000, or other external data system 12), version information, processingcapabilities, etc. For example, for “my_dep_3_search_5,” an externalquery configuration file can include the following entries referring toone of the secondary data intake and query systems 16:

[federated:my_dep_3_search_5] search = “search index=airlinedata | statscount by FlightNum” deployment_name = remote_deployment_3 hint =reporting maxResultCount=1000000 numFields = 2 [remote_deployment_3] IP= 10.183.45.30 Port = 8089 serviceAccount = eva_emerson password =changed Type = Splunk version = 10.1.4.6

As another example, for “my_dep_2_search_6,” an external queryconfiguration file can include the following entries referring to one ofthe third-party data storage and processing systems 5000:

[federated:my_dep_2_search_6] search = “SELECT COUNT (DISTINCTFlightNum) FROM airlinesdata” deployment_name = remote_deployment_2 hint= reporting maxResultCount=500000 numFields = 1 [remote_deployment_2] IP= 10.125.13.72 Port = 8089 serviceAccount = eliza_emmeline password =changed Type = SQL version = 6.4.0

Using the information in the external query configuration file, thesearch process service 3702 can determine that the search “searchindex=airlinedata|stats count by FlightNum” is to be executed on“remote_deployment_3,” which is a “Splunk” system, version 10.1.4.6,that is accessible via port 8089 at the IP address 10.183.45.30 usingthe eva_emerson service account. Moreover, the search process service3702 can determine that executing this search will return a maximumnumber of 1,000,000 records or events and that the search may use nomore than two fields to process the received records.

Similarly, using the information in the external query configurationfile, the search process service 3702 can determine that the search“SELECT COUNT (DISTINCT FlightNum) FROM airlinesdata” is to be executedon “remote_deployment_2,” which is an “SQL” system, version 6.4.0, thatis accessible via port 8089 at the IP address 10.125.13.72 using theeliza_emmeline service account. Moreover, the search process service3702 can determine that executing this search will return a maximumnumber of 500,000 records or events and that the search may use no morethan one field to process the received records.

Moreover, using the information in the external query configurationfile, the search process service 3702 can generate at least a portion ofa subquery for the external data systems 12, and/or generate one or morequery instructions for the worker nodes 3306 or external data systems12. In addition, in certain cases, the search process service 3702 canassign a primary search identifier to each subquery to enable theprimary data intake and query system to identify and distinguish partialresults from different external data systems 12. With reference to theexample above, the search process service 3702 can assign one primaryidentifier to the federated:my_dep_3_search_5 search and a differentprimary identifier to the federated:my_dep_2_search_6 search.

In addition, as part of parsing the query or query processing phase5102, the search process service 3702 can receive a resource allocationfor the query. The resource allocation can indicate an amount of memory,processors, and/or worker nodes 3306 that will be made available for thequery. The search process service 3702 can use the resource allocationto further generate instructions for the worker nodes 3306 and/orsubqueries for the external data systems 12.

As described herein, the resource allocation can be based on the numberof processors and amount of memory in the data intake and query system,the number of worker nodes 3306 in the data intake and query system, theamount of data being ingested and number of searches being executed bythe data intake and query system, the number of searches that the dataintake and query system is configured to execute, etc. For example, ifeach machine 3402 includes 48 processors and 12 TB of memory and isconfigured to handle 12 concurrent searches, then each machine 3402 canprovisionally allocate 4 processors and 1 TB of memory for each search.In some cases, the allocated processors and memory from a particularmachine can be referred to as a worker node 3306. Thus, with continuedreference to the previous example, if there are ten machines, then tenworker nodes, each with 4 processors and 1 TB of memory can beprovisionally allocated for each search.

Further, the search process service 3702 can receive an identificationof one or more worker nodes 3306 that can be used to communicate withthe external data systems 12, and map the worker nodes 3306 to one ormore external data systems 12 for communication purposes. Each workernode 3306 can be mapped to one or more external data systems 12.

At (4), the search process service 3702 communicates a request for adata ingest estimate to the worker nodes 3306. The data ingest estimatecan refer to the amount of data that is expected to be received from thedifferent external data systems 12. In some cases, the request for adata ingest estimate can include a request for a record or event countor the number of events or records that are expected to be received froman external data system 12 based on the subquery to be sent to theexternal data system 12. In certain embodiments, the data ingestestimate can include a request to provide an estimated size of the data(non-limiting example: amount of memory required to store the data) tobe ingested from the external data system 12.

At (5A), the worker nodes 3306 determine the data ingest estimate inconjunction with the external data system 12. As mentioned, in certaincases, the worker nodes 3306 are mapped to external data systems 12 forcommunication purposes, such as for control layer communications.Accordingly, a worker node 3306 can establish communication with anexternal data system 12 to determine the data ingest estimate for theexternal data system 12.

In some cases, as part of determining the data ingest estimate, theworker node 3306 determines the functionality or version number of theexternal data system 12 and determines the estimate based on thedetermined functionality or version. For example, in some cases, theexternal data system 12 may be able to dynamically determine and returna data ingest estimate based on search parameters that it parses fromthe subquery. In other cases, the external data system 12 may not beable to parse the subquery, but may be able to dynamically determine andreturn a data ingest estimate based on search parameters received fromthe worker node 3306 after the worker node 3306 (or search processservice 3702) has parsed the subquery. In yet other instances, neitherthe worker node 3306 nor the external data system 12 may be able toparse the subquery or dynamically determine the data ingest estimatebased on the subquery. For example, in some cases, a third-party datastorage and processing system 5000 may be incapable of providing queryresult estimates or may be incapable of determining a result estimateseparately from performing the query. In some such cases, the searchprocess service 3702 may use a pre-determined or static data ingestestimate.

As mentioned, in some cases, the worker node 3306 can send the subqueryto the external data system 12 and request the external data system 12to return an estimate. In such cases, the external data system 12 canparse the subquery to identify relevant search parameters, such as, butnot limited to, partitions, tables, directories, inverted indexes, orindexes to be searched, time ranges of potentially relevant results,etc.

The external data system 12 can use the identified search parameters toidentify potentially relevant results. For example, the external datasystem 12 can parse the subquery to identify an index (also referred toherein as a partition) to be searched as part of the query and a timerange of potentially relevant results. Using the index and time range,the external data system 12 can identify records that overlap with thetime range. In certain embodiments, the external data system 12 can,using the index and time range, identify buckets in the index thatinclude events or records that overlap with at least a portion of thetime range and return the number of events in the identified buckets asthe data ingest estimate. As a non-limiting example, the external datasystem 12 may support a DBInspect command that uses an identified index,start time, and end time to identify a count of potentially relevantevents in buckets that at least partially fall within the start time andlast time and that are located in the identified index. In some cases,the count may be previously determined and stored, such as in anexternal query configuration file or inverted index. In certain cases,the external data system 12 can perform a count on the identifiedbuckets.

It will be understood that a variety of methods can be used by theexternal data system 12 to determine and return the data ingestestimate. For example, the external data system 12 can use an invertedindex or summary table to identify potentially relevant results, etc. Incertain cases, the external data system 12 can estimate an amount ofmemory used to store potentially relevant records and return the amountof memory as the data ingest estimate, etc.

As also mentioned, in some cases, the worker node 3306 (or searchprocess service 3702) can parse the query to identify relevant searchparameters, and communicate the search parameters to the external datasystem 12 with a request for the data ingest estimate. For example, insome cases, the external data system 12 may be unable to parse thesubquery received from the worker node 3306 and identify relevant searchparameters, but may be able to use search parameters received from aworker node 3306 to identify and return a data ingest estimate. Withcontinued reference to the DBInspect example above, the worker node 3306(or search process service 3702) can identify a relevant index orpartition, start time, and end time and communicate those parameters tothe external data system 12 along with a DBInspect command. Using theparameters, the external data system 12 can determine and return a dataingest estimate. However, it will be understood that other commands orsearch parameters can be used to determine the data ingest estimate.

Furthermore, in some instances, neither the worker node 3306 nor theexternal data system 12 may be able to parse the subquery to identifyquery parameters. For example, the subquery may include references tosystem-specific objects, metadata, or definitions of the external datasystem 12 that cannot be interpreted or understood by the worker node3306, and the external data system 12 may be unable to accept and parsethe subquery from the worker node 3306 for data ingest estimatepurposes. In such cases, the worker node 3306 (or search process service3702) can determine a data ingest estimate based on a predeterminedestimate, such as an estimate located in an external query configurationfile. With reference to the query example provided above, in the eventneither the assigned worker node 3306 nor external data system 12 canparse the subquery and dynamically determine the data ingest estimate,the worker node 3306 can indicate to the search process service 3702that no data ingest estimate could be determined or that a searchparameter is to be used as the data ingest estimate, such as themaxResultCount of 1,000,000. In some cases, such as when a searchparameter is used as the data ingest estimate, the search processservice 3702 can determine the search parameter by parsing an externalquery configuration file that includes the search parameter. In certaincases, the search parameter used as the data ingest estimate can beincluded in the query or subquery itself.

At (5B), the worker nodes 3306 can obtain system-specific searchconfiguration data from the external data system 12. The searchconfiguration data, which may also be referred to as search parameterconfiguration data, subquery structure or syntax data, or knowledgeobjects, can include information specific to the external data systems12, such as definitions, metadata, query processing instructions,macros, or conversion tables for expanding a query string for execution.Further, in some embodiments, the search parameter configuration datacan include instructions for parsing one or more search parameters ofthe query or subquery. For example, search configuration data in oneexternal data system 12 could provide the definition of the string“search1” in a query or subquery to be “search index=myIndex|sort-cFlightNum|head 1000” and the search configuration data in a differentexternal data system 12 could provide the definition of the string“search1” to be “search index=airlinesdata7m|stats count by ArrDelay.”As another example, where the external data system 12 is a third-partydata storage and processing system 5000, the external data system 12could provide the definition of the string “search1” to be “SELECT COUNT(DISTINCT FlightNum) FROM airlinesdata.” In any of the cases, the workernode 3306 or primary data intake and query system may be unable to parseor determine the meaning of the search parameter “search1” without theaid of the search configuration data or search parameter configurationdata for particular external data systems 12.

In some cases, depending on the version, capabilities, and/orfunctionality of the external data system 12 and the permissions orauthorizations granted to the primary data intake and query system (oruser thereof) to access the external data systems 12, an assigned workernode 3306 can obtain search configuration data related to the externaldata system 12. For example, the sharing of search configuration datamay be prohibited or not be supported by a particular external datasystem 12. Similarly, only some search configuration data may be madeavailable by the external data system 12 for use by worker nodes 3306 ora primary data intake and query system based on the authorizationsassociated with the primary data intake and query system or a userthereof.

To determine which search configuration data to retrieve, the workernode 3306 can provide the external data system 12 with the subquery thatis to be run on it. The external data system 12 can parse the subquery,identify portions of the query that have corresponding searchconfiguration data, and retrieve and return the search configurationdata to the worker node 3306. The retrieved search configuration datamay correspond to an ingest phase of the query or subquery or processingphase (e.g., join, reduction operation, etc.). In some cases, the searchprocess service 3702 or worker node 3306 can parse the subquery toidentify search parameters that it cannot understand or interpret andcommunicate the identified search parameters to the external data system12. In certain cases, the worker node 3306 can request the external datasystem 12 to return any portion or all search configuration data or anyportion or all search configuration data that is accessible based on theaccount or user credentials used to access the external data system 12.In certain embodiments, the external data system 12 verifies thecredentials or authorizations of the primary data intake and querysystem or a user thereof prior to making the search configuration dataavailable.

In some embodiments, the external data system 12 can return atransformed subquery to the worker node 3306 with the data ingestestimate and/or the search configuration data. For example, in somecases, to determine the data ingest estimate, the external data system12 can transform the subquery received from the worker node 3306. Forexample, as described herein, the subquery received from the worker node3306 may include references to search configuration data that isspecific to the external data system. The external system 12 cantransform the subquery using the search configuration data. In certainembodiments, the transformed subquery can be sent to the worker node3306 along with the data ingest estimate and/or the search configurationdata. Further, in some embodiments, the subquery returned by theexternal data system can refer to additional search configuration data.This additional search configuration data can be returned to enable theworker node 3306 and/or query coordinator 3304 to process the subqueryand generate a subquery for execution by the external data system 12.For example, the subquery may, as part of a later search phase, refer tosystem-specific search parameters, that would not be understandable by aworker node 3306 during query execution. Accordingly, the external datasystem 12 can communicate relevant search configuration data to theworker node 3306 to enable the worker node 3306 to process the variousphases of the query or subquery.

In some embodiments, the external data system 12 may not be capable ofunderstanding syntax generated by the data intake and query system 16A.For example, a third-party data storage and processing system 5000 maynot understand a query, subquery, or other command generated by the dataintake and query system 16A that uses SPL. In some such embodiments, thedata intake and query system 16A, or elements thereof (e.g., the searchprocess service 3702 or worker node 3306) may use configuration data oran external query configuration file associated with the external datasystem 12 to convert the query, sub-query, or command to a formatunderstood by the external data system 12, such as Lucene or SQL.

At (6), the worker nodes 3306 return the data ingest estimate and/orsearch configuration data from one or more external data systems 12 tothe search process service 3702. It will be understood that the dataingest estimate and method for obtaining it may be different acrossdifferent external data systems 12. For example, one external datasystem 12 may be able to determine the data ingest estimate by parsing asubquery received from a worker node 3306, another external data system12 may determine the data ingest estimate based on search parametersreceived from a worker node 3306, while a third external data system 12may be unable to determine the data ingest estimate. In any case, theworker nodes 3306 can provide the data ingest estimates, or lackthereof, to the search process service 3702. Furthermore, the workernodes 3306 can provide the search process service 3702 with the searchconfiguration data, if any, received from the external data systems 12.

At (7), the search process service 3702 continues the query processingphase 5102 by determining a size and quantity of tasks or partitions tobe performed as part of ingesting data from the secondary did take querysystems. During query execution, if too much data is being operated onby a particular processor, the processor may run out of memory and maystore some data or results to disk, which can significantly increase theexecution time of the query. As such, in certain embodiments, the searchprocess service 3702 can select a particular partition size in order toreduce the likelihood of spilling data to disk.

In some cases, the search process service 3702 determines the size ofthe partitions based on resources that have been allocated to executethe query and search parameters parsed from the query itself. Forexample, the size of the partitions can be determined based on thenumber of processors 3406 allocated for the query, an amount of memoryallocated for the query, and/or the number of fields of the records tobe analyzed as part of the query or subquery. As mentioned previously,the processor and memory allocation can be based on the amount ofprocessors and memory available to the system 16 as a whole, andconfiguration for the number of concurrent searches that are to besupported by the system 16. The number of fields can be determined byparsing the query or subquery. For example, if a subquery identifies twofields that will be used to process events, the search process service3702 can determine that two fields will be used as part of the query. Itwill be understood that a variety of mechanisms can be used to identifythe number of fields for the query or subquery and/or to determine thesize of the partitions. For example, in some cases, the search processservice 3702 can use an estimated size or average size of the records ordata that is to be processed, or the number of field can be included inan external query configuration file. Moreover, the search processservice 3702 can use other search parameters to determine the size andquantity of tasks. For example, the search process service 3702 can usean average size or estimate size of each record to be received, etc.

Furthermore, the relationship between the size of the partition and thedata used to determine the size can vary. For example, in some cases, asthe amount of memory allocated for the search increases relative to thenumber of processors, the size of partitions can increase. In certainembodiments, as the number of processors increases relative to theamount of memory, the size of the partitions can decrease. In someembodiments, as the number of fields increases, the size of thepartitions can decrease.

Based on the determined size of the partitions or tasks and the dataingest estimates corresponding to the various external data systems 12,the search process service 3702 can determine the number of estimatedpartitions or tasks to be executed as part of the ingestion of data fromthe external data systems 12. In certain embodiments, the number oftasks can be determined by dividing the data ingest estimate by the sizeof the partitions.

Further, the number and size of partitions can be used to estimate thesize of and duration for executing the query. In some cases, if thesearch process service 3702 determines that the size of the querysatisfies a size threshold or the duration for executing the querysatisfies a duration threshold, it can abandon the query or notify auser that the query will take longer than a threshold amount of time. Incertain embodiments, if the search process service 3702 determines thatthe size of the query satisfies the size threshold or the duration forexecuting the query satisfies a duration threshold, the search processservice 3702 can request that additional resources be allocated, such asadditional memory and/or processors. In this way, the search processservice 3702 can dynamically respond to queries of different sizes inorder to return results in a performance manner. Moreover, if additionalresources are allocated, the search process service can determine thesize and number of tasks to be executed based on the additionalresources.

In embodiments where the search process service 3702 is unable todetermine an estimated size or number of entries because, for example,the external data system 12 does not provide a data ingest estimate, thesearch process service 3702 may allocate a default number of resourcesor partitions. Further, in some embodiments, the search process service702 may dynamically adjust the allocated resources or partitions duringperformance of the query based, for example, on results being obtainedas the query, or sub-query, is executed.

At (8), the search process service 3702 generates query instructions forthe worker nodes 3306. In some embodiments, generating queryinstructions can include generating subqueries for the external datasystems 12, processing and/or optimizing the subqueries for thedifferent external data system 12 and/or worker nodes 3306, etc. Similarto determining the data ingest estimate, generating subqueries for theexternal data systems 12 can be based on the versions, functionality,and capabilities of the external data systems 12. For example, in anembodiment where a worker node 3306 is able to obtain searchconfiguration data for a particular external data system 12, the searchprocess service 3702 can use the obtained search configuration data togenerate the subquery. Thus, the subquery can be transformed into anative state for execution by the external data system 12. Such atransformation can reduce the workload of the external data system 12.For example, in some embodiments, the transformation may reduce theamount of processing performed by a search head 210 or controller of theexternal data system. Further, in embodiments where the external datasystem 12 includes a third-party data storage and processing system5000, associated configuration data or an associated configuration filecan provide conversion information that enables the data intake andquery system 16A to convert a query or subquery into a languageunderstood by the third-party data storage and processing system 5000.The configuration file may include one or more entries that translatethe sub-query from SPL to a query language understood by the third-partydata storage and processing system 5000, such as Lucene, JSON, or SQL.Alternatively, or in addition, the configuration file may identify atype of the third-party data storage and processing system 5000 and/or alanguage (e.g., query language) understood or supported by thethird-party data storage and processing system 5000. Based on theidentity of the third-party data storage and processing system 5000and/or a language understood or supported by the third-party datastorage and processing system 5000, the data intake and query system 16Acan translate or transform a query, sub-query, or command to be providedto the third-party data storage and processing system 5000 to thelanguage supported or understood by the third-party data storage andprocessing system 5000. For example, the data intake and query system16A may translate or transform an SPL query or sub-query to an SQLquery.

In some embodiments, the search process service 3702 can generate thesubquery or perform the transformation of the subquery for the externaldata system 12. In certain embodiments, the search process service 3702includes instructions for a worker node 3306 in communication with theexternal data system 12 to generate the subquery or perform thetransformation. By enabling or assigning a worker node 3306 to performthe transformation, the processing by the search process service 3702can be reduced.

In certain cases, such as when search configuration data cannot beretrieved from an external data system 12, the search process service3702 can generate, determine, or use subqueries that can be furthertransformed by the respective external data system 12. For example, thesearch process service 3702 can determine that a subquery identifiedfrom the query or an external query configuration file is to be used fora particular external data system 12, and communicate the identifiedsubquery to the external data system 12. In such cases, the externaldata system 12 can transform the subquery for execution, including usingrelevant search configuration data to expand the search parameters orgenerate a native query.

In addition, in certain embodiments, a query or subquery may not includereference to search parameters specific to a particular external datasystem 12 or the query or subquery can be processed without reference tosearch configuration data from the particular external data system 12.In such embodiments, the search process service 3702 can determine orgenerate a subquery for the external data system 12 and may not requestsearch configuration data from the worker node 3306.

Further, depending on the capability of the external data systems 12,the search process service 3702 can include instructions for theexternal data system 12 to send partial results to a single worker node3306 or distribute results of the subquery to multiple worker nodes3306. For example, an external data system 12 may not have thefunctionality or ability to partition results amongst multipledestinations. In such embodiments, the search process service 3702 caninclude instructions for the external data system 12 to communicate allresults to a particular worker node 3306. In turn, the assigned workernode 3306 can distribute the results to multiple worker nodes 3306 (insome cases, including itself). In such embodiments, the search processservice 3702 can include instructions for a daemon operating on theexternal data system 12 to send the results to the particular work node3306. In such cases, the external data system 12 (non-limiting example:search process service 3702 of a secondary data intake and query system)can, after executing the query, store the results to disk. The daemoncan pull the results from the disk and send them to the assigned workernode 3306.

In embodiments where the external data system 12 can partition, ordistribute, results amongst multiple destinations, the search processservice 3702 can include an instruction for the external data system 12to do so. In some embodiments, the instruction can be an instruction fora search process service 3702 of a secondary data intake and querysystem to send results from the indexers (or worker nodes 3306) of thesecondary data intake and query system to the worker nodes 3306 of theprimary data intake and query system 16 without storage of the resultsto disk. Furthermore, the search process service 3702 can assign workernodes 3306 to receive results from the various external data systems 12.In some embodiments, the primary data intake and query system 16A mayinstruct one external data system 12 to provide query results to anotherexternal data system 12, which can then use the query results to performfurther operations or queries. For example, the data intake and querysystem 16A may instruct the external data system 12-1 to provide queryresults to the external data system 12-2. In some such embodiments, theprimary data intake and query system 16A may instruct the external datasystem to convert the query results, or a sub-query that includes thequery results, from one format to another format prior to providing thequery results to another external data system. For example, the primarydata intake and query system 16A may instruct the external data system12-1 to convert a sub-query and/or query results from an SPL format toan SQL format prior to providing the sub-query and/or query results tothe external data system 12-3.

The instructions to distribute results amongst multiple worker nodes3306 can include instructions as to how the results are to bedistributed. As described herein, a variety of mechanisms can be used todistribute results between the worker nodes 3306. For example, thesearch process service 3702 can include instructions to distribute theresults in a round robin, random, or particular order. In some cases,the search process service 3702 can instruct the external data system 12to perform a hash on the results and based on the hash send the resultsto a particular worker node 3306. As a non-limiting example, the searchprocess service 3702 can include instructions for the external datasystem 12 to use a modulo operand on the data to be distributed todetermine to which worker node 3306 that data is to be assigned.However, it will be understood that a variety of mechanisms can be usedto distribute partial results among worker nodes 3306. For example, insome cases, the external data system 12 can determine the manner inwhich results are to be distributed between worker nodes 3306.

As mentioned, in some cases, the worker nodes 3306 can be shared betweenthe primary data intake and query system and the external data system12. In such embodiments, the search process service 3702 can includeinstructions for the external data system 12 to send results from theworker nodes 3306 of the external data system 12 to the worker nodes3306 of the primary data intake and query system 16A. During execution,in embodiments where the worker nodes 3306 are shared between theprimary data intake and query system and external data systems 12,worker nodes 3306 can be assigned to reduce the communication of dataover a network or between machines. Accordingly, in certain embodiments,an instruction from the external data system 12 to transmit results fromone worker node 3306 to another worker node 3306 can result in the sameworker node 3306 retaining the data.

As part of generating the query instructions, the search process service3702 can designate worker nodes 3306 to receive results from theexternal data systems 12. Further, the search process service 3702 canreach out to the worker nodes 3306 and obtain communication informationor network access information, such as, but not limited to, a device,network or IP address, or port number, etc., so that the external datasystems 12 can send the data directly to the worker nodes 3306.Moreover, the search process service 3702 can instruct the worker nodes3306 to set up buffers or other receivers to receive the partial resultsfrom the external data systems 12. Moreover, the search process service3702 can further process and/or optimize the query or subqueries forexecution by the worker nodes 3306. For example, the search processservice 3702 can request that the worker nodes 3306 be located on thesame machine to reduce network traffic, etc.

As part of the query execution phase 5104, the search process service3702 can (9) communicate the query instructions to the worker nodes3306. As described herein, the query instructions can include sufficientinformation to enable the worker nodes 3306 to execute the query,including instructions to communicate any subqueries to the externaldata systems 12. In some embodiments, the search process service 3702can include a mapping of worker nodes 3306 to particular external datasystems 12. The worker node 3306-external data system 12 mapping can bethe same as or different from the mapping used to obtain data ingestestimates from the external data system 12. For example, the mappingused to obtain data ingest estimates may use any available worker node3306, while the mapping for the query execution phase 5104 may be amapping to one of the worker nodes 3306 allocated for the query. Incertain embodiments, the search process service 3702 can includeinstructions for the worker nodes 3306 to determine the mapping betweenthe worker nodes 3306 and the external data systems 12.

In accordance with the received instructions, the worker nodes 3306 canexecute the query, which can include (10) distributing the subqueries tothe external data systems 12. Distributing the subqueries to theexternal data systems 12 can include translating the query from onelanguage to another language based, for example, on a mapping betweenquery terms or a system-type identifier included in an external queryconfiguration file. As described herein, as part of executing the query,the worker nodes 3306 can gather and process data from other datasets,such as data from indexers 206 of the primary data intake and querysystem.

At (11), the external data systems 12 execute the subquery. The externaldata systems 12 can process and execute the query in a manner similar tothe processing and execution of the federated query by the primary dataintake and query system. For example, in some embodiments, the externaldata systems 12 can parse the subquery to identify relevant data to besearched, generate subqueries for components of the external datasystems 12, such as, but not limited to, indexers 206 (or other queryexecutors), and obtain the relevant data and process it according to thesubquery received from the worker nodes 3306. Furthermore, inembodiments where an external data system 12 includes worker nodes 3306,the external data system 12 can generate query instructions for theworker nodes 3306.

In addition, as part of processing the subquery, the external datasystem 12 can assign a local search identifier to the search. Forexample, the external data system 12 can assign search identifiers toall searches that it receives in order to identify and distinguishbetween the different processes and results of each search. Moreover,when the external data system 12 communicates partial results to theworker node 3306, it can include the local search identifier that itassigned in each data chunk that it communicates to the worker node3306. In some cases, based on the local search identifier, the workernode 3306 can distinguish between partial results received fromdifferent external data systems 12.

As described herein, in certain embodiments, such as when the workernodes 3306 are able to obtain search configuration data of a particularexternal data systems 12, the worker nodes 3306 can perform some of thetasks that would otherwise be performed by the search head 210 orcontroller of an external data system 12. For example, a worker node3306 can parse the subquery and generate instructions for indexers 206(or query executors) of the external data system 12. In this manner, aworker node 3306 can reduce the processing performed by the externaldata system 12.

At (12), the worker nodes 3306 receive the subquery results or partialresults from the external data systems 12. As described herein, in somecases, one worker node 3306 can receive the partial results from aparticular external data system 12 and distribute the results tomultiple worker nodes 3306. As further described herein, the partialresults from a particular data intake and query system can bedistributed to various worker nodes 3306 in a variety of ways. Incertain embodiments, multiple worker nodes 3306 can receive partialresults from a particular external data system 12 and/or one worker node3306 can concurrently receive partial results from multiple externaldata systems 12. As mentioned, data chunks corresponding to the partialresults from each external data system 12 can include a local searchidentifier that uniquely identifies the search to which the data chunkbelongs within the external data system 12. In certain embodiments, theexternal data system 12 and/or the worker nodes 3306 may translate ortransform query results from a format or language supported by theexternal data system 12 to a format or language supported by the dataintake and query system 16A. The external data system 12 and/or theworker nodes 3306 may determine the supported format to convert thequery results based on an entry in an external query configuration fileof the external data system 12 and/or of the data intake and querysystem 16A.

At (13), the worker nodes 3306 process the results of the subqueries. Asdescribed herein, the worker nodes 3306 can concurrently process partialresults received from different external data systems 12. Furthermore,the worker nodes 3306 can perform additional processing on partialresults from one external data system 12 alone or in combination withpartial results received from the other external data system 12. Theprocessing of partial results by the worker nodes 3306 can be done inaccordance with the query instructions received from the search processservice 3702. Further, the additional processing may include convertingor transforming the partial results or query results from one formatsupported by the external data system 12 to another format supported bythe data intake and query system 16A.

Although not illustrated in FIG. 51 , it will be understood that thesearch process service 3702 can monitor the nodes 3306 and dynamicallyallocate resources based on the monitoring. For example, if more partialresults are received from the external data systems 12 than wereexpected, the search process service 3702 can request additionalprocessors and/or worker nodes 3306 to ingest and process the partialresults. Similarly, if fewer partial results are received than wasexpected, the search process service 3702 can de-allocate processorsand/or worker nodes 3306.

In addition, during execution, the worker nodes 3306 can communicatewith each other to process the partial results in a distributed manner.If, for example, one worker node 3306 receives a larger portion of thepartial results than other worker nodes 3306 and/or begins to lag inprocessing its partial results, the worker nodes 3306 can dynamicallyre-assign data or tasks between the worker nodes 3306 for execution.

In some cases, the worker nodes 3306 use a mapping of the primary searchidentifier (assigned to subqueries by the primary data intake and querysystem 16) to the local search identifiers (assigned by an external datasystem 12 to the subquery that it executed) to identify and process thepartial results. As described herein, the primary data intake and querysystem 16A can assign primary search identifiers to logically identifythe different subqueries that will be operated on by the differentworker nodes 3306. Similarly, the external data systems 12 can assignlocal search identifiers to the subquery to uniquely identify thesubquery (and its result) from other queries that the external datasystem is executing. Accordingly, the same subquery may be referred toby the primary data intake and query system 16A using a primary searchidentifier that does not match the local search identifier that is usedby the external data system 12 to identify the subquery.

To address the mismatch, as the external data systems 12 assign localsearch identifiers to the subquery, they can communicate the assignedlocal search identifier to the primary data intake and query system 16A(e.g., via the worker node 3306). In turn, the primary data intake andquery system 16A can map the local search identifier assigned to asubquery by the external data system to the primary search identifierassigned to the same subquery by the primary data intake and querysystem 16A. Thus, as a worker node 3306 receives and processes partialresults from different external data systems 12 it can use the mappingto determine what transformations (based on instructions from the searchprocess service 3702 that refer to the subquery using the primary searchidentifier) are to be performed on the partial results from differentexternal data systems 12 (which refers to the partial results using thelocal search identifier).

At (14), the worker nodes 3306 communicate the results of the processingto the search process service 3702 or to another dataset destination asdescribed herein. At (15), the search process service 3702 can performadditional processing, and at (16) the results can be communicated tothe search head 210 for communication to the client device. In somecases, prior to communicating the results to the client device, thesearch head 210 can perform additional processing on the results.

It will be understood that the query data flow can include fewer or moresteps. For example, in some cases, the search process service 3702 doesnot perform any further processing on the results and can simply forwardthe results to the search head 210. In certain embodiments, nodes 3306receive data from multiple dataset sources 3704, etc.

Although not shown in FIG. 51 , it will be understood that primary dataintake and query system can concurrently execute a local search, theresults of which, can be combined with the partial results of theexternal data system 12. In some embodiments, partial results from alocal search can be combined with partial results from the external datasystems 12 by the worker nodes 3306, the search process service 3702, orthe search head 210.

Moreover, it will be understood that the various functions described canbe performed concurrently or in any order. For example, search processservice 3702 can generate query instructions before, after, orconcurrently with determining a size and quantity of partitions or tasksand/or requesting or obtaining data ingest estimates, etc.

26.0. Search of Secondary Data Intake and Query System Flow

FIG. 52 is a flow diagram illustrative of an embodiment of a routine5200 implemented by a query coordinator 3304 to execute a queryinvolving data from a secondary data intake and query system. Althoughdescribed as being implemented by the query coordinator 3304, it will beunderstood that one or more elements outlined for routine 5200 can beimplemented by one or more computing devices/components that areassociated with a data intake and query system 16, such as the searchhead 210, search process master 3302, indexer 206, and/or worker nodes3306. Thus, the following illustrative embodiment should not beconstrued as limiting.

At block 5202, the query coordinator 3304 receives a query, as describedherein at least with reference to block 3802 of FIG. 38 . At block 5204,the query coordinator 3304 identifies one or more external data systems.In some embodiments, the external data systems can be one or moresecondary data intake and query system 16 or 108. As described herein,in some embodiments, the query can include an indicator that it is afederated query. Based on the indication that the query is a federatedquery, the query coordinator 3304 can identify one or more secondarydata intake and query systems 16B and/or 16C and/or one or morethird-party data storage and processing systems 5000 that are to be partof the search. For example, the query can include a command indicatingthat an external data system is to be used or searched and/or a subqueryis to be executed by an external data system. Based on identification ofthe command, the query coordinator 3304 can look up or otherwiseidentify the external data system that is to be searched, used or is toexecute the subquery. For example, the data intake and query system caninclude an external query configuration file that provides additionalinformation, such as the name and location of external data systemsassociated with the primary data intake and query system 16A, accessinformation for the external data system, query languages supported bythe external data system, etc.

In certain embodiments, the query can explicitly identify a secondarydata intake and query system or a third-party data storage andprocessing system 5000 that is to execute a subquery. In certain cases,the query coordinator 3304 parses the query to identify the externaldata system. For example, the query may include the name (or otheridentifier) or the location (e.g., IP address, port, access protocol) ofthe external data system.

At block 5206, the query coordinator 3304 generates a subquery for thesecondary data intake and query system.

Similar to the identification of the external data system, the querycoordinator 3304 can identify a subquery for the external data system byparsing the query. In some embodiments, the query can include thesubquery that is to be executed by the external data system. In certainembodiments, the query can include a reference and the query coordinator3304 can refer to an external query configuration file or other locationto identify the subquery that is to be executed by the external datasystem. The query coordinator 3304 may identify the subquery in theexternal query configuration file based on the reference included in thequery.

Based on the identification of the subquery, the query coordinator 3304can generate a subquery for the external data system. As part ofgenerating the subquery for the external data system, the querycoordinator 3304 can request search configuration data from the externaldata system. As described herein, the search configuration data caninclude definitions and/or additional search parameters that arespecific to the external data system. For example, the subqueryidentified by the query coordinator 3304 may reference an instructionset, macro, or naming convention that is not understood or known by thequery coordinator 3304, but is understood by the external data system.Accordingly, the query coordinator 3304 can request the instruction set,macro information, or naming convention from the external data system,and use this information to generate the subquery that is to be executedby the external data system.

In addition, the query coordinator 3304 can request a version number orother indications of the capabilities of the external data system. Forexample, the query coordinator 3304 can request the external data systemto provide information as to the number or amount of processingresources it has available. Based on this information, the querycoordinator 3304 can generate the subquery to increase or decrease theamount of processing performed by the external data system. For example,if the query coordinator 3304 determines that the external data systemwill take too long to process data or has insufficient resources toprocess the data within a particular time frame, the query coordinator3304 can generate a subquery for the external data system to reduce theamount of processing performed thereon. For example, rather thaninstructing the external data system to perform multiple transformationson its data, the query coordinator 3304 can instruct the external datasystem to send the data to the worker nodes 3306 without performing anytransformations or performing a limited number of transformations.

Similarly, the query coordinator 3304 can, based on the version orcapabilities of the external data system, generate the subquery toinstruct the external data system to distribute its results acrossmultiple worker nodes 3306 or communicate its results to a singleworker. In some embodiments, such as when the external data system is tosend the results to a single worker node 3306, the query coordinator3304 can instruct the worker node 3306 to distribute the results acrossmultiple worker nodes 3306.

In some embodiments, as part of generating or determining a subquery forthe external data system, the query coordinator 3304 can request a dataingest estimate from the external data system. Based on the estimate,the query coordinator 3304 can determine or estimate a number of tasksor partitions to use to ingest the data and determine whether additionalprocessing should be performed by the external data system prior tocommunicating the results. Further, the query coordinator 3304 can usethis information to determine whether additional worker nodes 3306should be allocated to process the results received from the externaldata system, estimate an ingest or search time, etc.

Accordingly, using information from the query and/or the external datasystem, the query coordinator 3304 can generate a subquery. However, itwill be understood that in some cases, the query coordinator 3304 caninstruct a worker node 3306 to generate a subquery for the secondarydata intake and query system. For example, the worker node 3306 may havethe search configuration data associated with the secondary data intakeand query system and be able to generate a subquery in a native formatfor the external data system. In some cases, by having a worker generatethe subquery, the system 16 can distribute processing tasks betweenmultiple processors and reduce the likelihood of creating a bottleneckat the query coordinator 3304.

As described herein, in certain embodiments, the query coordinator 3304generates a subquery that tasks the external data system with returningthe data, performing some processing of the data, or processing the dataas much as it can based on its capabilities.

At block 5208, the query coordinator 3304 generates instructions for theworker nodes 3306. In some cases, as part of generating instructions forthe worker nodes 3306, the query coordinator 3304 can instruct theworker nodes 3306 to set up or provide a location for the external datasystem to send results, such as a network address, MAC address, deviceidentifier, IP address, port number, or other network accessinformation, etc. In addition, the query coordinator 3304 can includeinstructions for the worker nodes 3306 to communicate the subqueries tothe external data system. In some cases, the query coordinator 3304 caninstruct the worker nodes 3306 to generate at least a portion of thesubquery for the external data system. For example, the querycoordinator 3304 can instruct the worker nodes 3306 to use the searchconfiguration data to generate the subquery for the external datasystem. In this way, the query coordinator 3304 can distribute someprocessing to the worker nodes 3306.

Moreover, the query coordinator 3304 can include instructions for theworker nodes 3306 to perform additional processing on the partialresults received from the external data system, combine partial resultsfrom multiple external data systems, and perform additional processingon the combined partial results. The query coordinator 3304 can alsoprovide the worker nodes 3306 with the data ingest estimate. The workernodes 3306 can use this information to configure themselves to processthe incoming data in a distributed manner.

As described herein, in certain embodiments, as part of generatinginstructions for the worker nodes 3306 (or generating the subqueries),the query coordinator 3304 can assign a primary search identifier foreach subquery and include the primary search identifier in theinstructions sent to each worker node 3306 to be mapped to local searchidentifiers received from the external data systems. As describedherein, the worker nodes 3306 can use the mapping to determine how toprocess data from particular external data systems.

At block 5210, the query coordinator 3304 executes the query. In somecases, as described herein, to execute the query, the query coordinator3304 communicates a query processing scheme or the generatedinstructions to the worker nodes 3306. In turn, the worker nodes 3306execute the instructions, which can include, communicating subqueries tothe external data systems, receiving partial results therefrom,processing the partial results, and returning results to the querycoordinator 3304. The query coordinator 3304 can perform processingbased on the query processing scheme and communicate the results to thesearch head 210 for display on the client device 404.

As described herein, in some embodiments, the external data systemprocesses and executes the subquery similar to the manner in which theprimary data intake and query system processes and executes the query.Further, the external data system can process and execute the subquerysimilar to the manner in which it executes other queries received from auser or client device, except that results are communicated to one ormore worker nodes 3306 instead of (or in addition) to a user or clientdevice. In some embodiments, as part of executing the subquery, theexternal data system can assign the subquery a local search identifierand communicate the local search identifier to the worker node 3306. Theworker node 3306 can map the local search identifier with the primarysearch identifier received from the primary data intake and query systemto determine how the partial results from the external data system areto be processed according to the instructions received from the primarydata intake and query system.

It will be understood that fewer, more, or different blocks can be usedas part of the routine 5200. For example, in some embodiments, theroutine 5200 can further include, monitoring nodes 3306 during queryexecution, allocating/deallocating resources based on the query, etc. Asanother example, in certain embodiments, identifying the secondary dataintake and query system, generating a subquery, and generatinginstructions for the worker nodes 3306 can form part of a processingquery block, similar to the process query block 3804 of FIG. 38 .Moreover, it will be understood that one or more blocks described hereinwith reference to routine 5200 can be combined with one or more blocksof other routines described herein, such as the routines describedherein at least with reference to FIGS. 5, 6, 23-26, 31, 34, 38-45, 47,49, 52-57, and 59 .

Furthermore, it will be understood that the various blocks describedherein with reference to FIG. 52 can be implemented in a variety oforders. In some cases, the system 16 can implement some blocksconcurrently or change the order as desired. For example, the system 16can concurrently generate a subquery for the secondary data intake andquery system (e.g., block 5206) and generate instructions for the workernodes 3306 (e.g., block 5208), or in any order, as desired. As yetanother example, the query coordinator 3304 can concurrently coordinatea search of data within the primary data intake and query system. Insome cases, the results from the query of data within the primary dataintake and query system can become linked with the partial resultsreceived from the secondary data intake and query systems.

27.0. Search with Data Ingest Estimate Flow

FIG. 53 is a flow diagram illustrative of an embodiment of a routine5300 implemented by the query coordinator 3304 to execute a query ondata from an external data system 12. Although described as beingimplemented by the query coordinator 3304, it will be understood thatone or more elements outlined for routine 5300 can be implemented by oneor more computing devices/components that are associated with a dataintake and query system 16, such as the search head 210, search processmaster 3302, indexer 206, and/or worker nodes 3306. Thus, the followingillustrative embodiment should not be construed as limiting.

At block 5302, the query coordinator 3304 receives a query, as describedherein at least with reference to block 3802 of FIG. 38 . At block 5304,the query coordinator 3304 identifies an external data system 12, asdescribed in greater detail herein at least with reference to block 3902of FIG. 39 and block 5204 of FIG. 52 . At block 5306, the querycoordinator 3304 dynamically generates a subquery for the external datasystem 12, as described in greater detail herein at least with referenceto block 4206 of FIG. 42 and block 5206 of FIG. 52 .

At block 5308, the query coordinator 3304 determines a data ingestestimate for the external data system 12, such as a secondary dataintake and query system. As described herein, the query coordinator 3304can determine the data ingest estimate for the external data system 12in a variety of ways. In some embodiments as part of determining a dataingest estimate, the query coordinator 3304 maps one or more workernodes 3306 to different external data systems 12 for communicationpurposes. The query coordinator 3304 requests the worker nodes 3306 todetermine a data ingest estimate for each of their assigned externaldata systems 12.

To obtain a data ingest estimate for a particular external data system12, the worker node 3306 can request the external data system 12 toreturn its version number or use other information to determine thefunctionality of the external data system 12. Based on the determinedfunctionality of the external data system 12, the worker node 3306 canobtain a data ingest estimate. For example, in some cases, the workernode 3306 can send the external data system 12 the subquery and theexternal data system 12 can return the data ingest estimate based on itsanalysis of the subquery. In certain cases the worker node 3306 canparse the query to identify one or more search parameters andcommunicate the search parameters to the external data system 12. Basedon the search parameters, the external data system 12 can determine andreturn a data ingest estimate. In some embodiments, neither the workernode 3306 nor the external data system 12 can parse the subquery toidentify relevant search parameters. For example, the subquery mayinclude commands or references that are not understood by the workernode 3306 or that are specific to the external data system and theexternal data system 12 may not support receiving and parsing a subqueryfrom the worker node 3306 to determine a data ingest estimate. In suchcases, the worker node 3306 can use a predetermined estimate as the dataingest estimate for the external data system 12. However, as describedherein, the worker node 3306 and/or external data system 12 can use avariety of techniques to determine the data ingest estimate.

The worker nodes 3306 can return the data ingest estimate to the querycoordinator 3304 for each external data system 12 assigned thereto.Based on the data ingest estimate from the various worker nodes 3306,the query coordinator 3304 can determine a data ingest estimate for thequery as a whole. This information can be used to estimate the size ofingest for the query and/or the time to ingest the data. In some cases,based on the data ingest estimate and the amount of resources allocatedfor the search, the query coordinator 3304 can determine that the querywill take longer than a threshold period of time. As such, the querycoordinator 3304 can request additional resources for the search and/orreject the search.

At block 5310, the query coordinator 3304 determines the size andquantity of partitions/tasks for an ingest stage. As described herein,the query coordinator 3304 can determine the size of each partition ortask based on resources allocated to it for the search and/or one ormore search parameters of the query. For example, the size of eachpartition can be based on the number of processors and amount of memoryallocated for the query and the number of fields used during the query.In addition, as described herein, the query coordinator 3304 candetermine the number of partitions based on the data ingest estimate andthe partition size. However, as described herein, it will be understoodthat the query coordinator 3304 can use a variety of techniques todetermine the size and quantity of the partitions for the ingest stage.

At block 5312, the query coordinator 3304 generates instructions for theworker nodes 3306. As described herein, at least with reference to block5208 of FIG. 52 , the query coordinator 3304 can generate instructionsfor the worker nodes 3306 based on a variety of parameters and caninclude instructions to: distribute subqueries to external data systems12, receive local search identifiers used by the external data systems12 to identify their respective subqueries (and partial results), mapthe local search identifiers for subqueries to corresponding primarysearch identifiers, concurrently receive and process partial resultsfrom multiple external data systems 12 (in some cases based on the localsearch identifier-primary search identifier mapping), distribute partialresults from one multiple external data system 12 to multiple workernodes 3306, combine, and further process results, and communicate searchresults to the query coordinator 3304, etc.

In some embodiments, the instructions are generated based on thedetermined partition size and quantity. For example, the instructionscan inform the worker nodes 3306 as to the quantity and size ofpartitions. In this way, the worker nodes 3306 can be dynamicallyconfigured to process the results in a distributed manner. In someembodiments, based on the partition size and quantity, the worker nodes3306 (or query coordinator 3304) can allocate worker nodes 3306 withgreater processing resources to ingest data from secondary data intakeand query systems that are expected to output a larger amount of partialresults. In this way, the partial results can be received and processedin a performant manner. In addition, the query coordinator 3304 can usethe partition size and quantity to determine a time duration to executethe query, request additional resources, or deallocate resources, etc.For example, if the estimated time to execute the query exceeds athreshold amount of time or the estimated number of partitions exceeds athreshold number, the query coordinator 3304 can request additionalresources, notify a user, and/or cancel the query. Similarly, if theestimated number of partitions is less than a threshold amount, thequery coordinator 3304 can deallocate resources for use with otherqueries.

At block 5314, the query coordinator 3304 executes the query asdescribed in greater detail herein at least with reference to block 4010of FIG. 40 and block 5210 of FIG. 52 . It will be understood that fewer,more, or different blocks can be used as part of the routine 5300. Forexample, in some embodiments, the routine 5300 can further include,monitoring nodes 3306 during query execution, allocating/deallocatingresources based on the query, etc. As another example, in certainembodiments, the determination of the data ingest estimate and thepartition size and quantity can form part of a processing query block,similar to the process query block 3804 of FIG. 38 . Moreover, it willbe understood that one or more blocks described herein with reference toroutine 5300 can be combined with one or more blocks of other routinesdescribed herein, such as the routines described herein at least withreference to FIGS. 5, 6, 23-26, 31, 34, 38-45, 47, 49, 52, and 54-59 .

Furthermore, it will be understood that the various blocks describedherein with reference to FIG. 53 can be implemented in a variety oforders. In some cases, the system 16 can implement some blocksconcurrently or change the order as desired. For example, the system 16can concurrently generate a subquery for the external data system 12(5306) and instructions for the worker nodes 3306 or in any order, asdesired.

28.0. Search Using Search Configuration Data Flow

FIG. 54 is a flow diagram illustrative of an embodiment of a routine5400 implemented by the query coordinator 3304 to execute a query ondata from an external data system 12. Although described as beingimplemented by the query coordinator 3304, it will be understood thatone or more elements outlined for routine 5400 can be implemented by oneor more computing devices/components that are associated with a dataintake and query system 16, such as the search head 210, search processmaster 3302, indexer 206, and/or worker nodes 3306. Thus, the followingillustrative embodiment should not be construed as limiting.

At block 5402, the query coordinator 3304 receives a query, as describedherein at least with reference to block 3802 of FIG. 38 . At block 5404,the query coordinator 3304 identifies an external data system 12, asdescribed in greater detail herein at least with reference to block 3902of FIG. 39 and block 5204 of FIG. 52 .

At block 5406, the query coordinator 3304 obtains search configurationdata for the external data system 12. As described herein, the querycoordinator 3304 can obtain the search configuration data in a varietyof ways. For example, the query coordinator 3304 can obtain the searchconfiguration data using an external query configuration file and/or bycommunicating with the external data system 12.

In some embodiments, to obtain the search configuration data, the querycoordinator 3304 maps one or more worker nodes 3306 to differentexternal data systems 12 for communication purposes. The querycoordinator 3304 can instruct the worker nodes 3306 to request searchconfiguration data from each of their assigned external data systems 12.

The worker node 3306 can request the search configuration data in avariety of ways. For example, the worker node 3306 can request searchconfiguration data by sending the subquery to the external data system,sending unrecognized search parameters to the subquery, requesting allsearch configuration data associated with a particular user, etc.

In some embodiments, a worker node 3306 requests search configurationdata by sending the external data system 12 the subquery that it is toexecute. The external data system 12 can parse the subquery and returnsearch configuration data to the worker node 3306 so that the workernode can understand or interpret external data system-specific searchparameters in the subquery, such as macros, commands, or referencesspecific to the external data system 12.

In certain embodiments, the worker node 3306 requests searchconfiguration data by sending search parameters to the external datasystem 12, such as macros, commands, or references, in the subquery thatit (or the query coordinator 3304) is unable to parse, interpret, orunderstand. The external data system 12 can return the correspondingsearch configuration data to enable the worker node 3306 to interpretsearch parameters specific to the external data system 12.

In some cases, the worker node 3306 can request search configurationdata that is associated with a particular user or account. For example,each user or account may have different authorizations or permissions onthe external data system 12. Accordingly, the worker node 3306 can usethe authorizations or permissions of a specific account or user torequest the search configuration data that the particular user oraccount is allowed to access. In response, the external data system 12can return the search configuration data associated with the requestedaccount or user. Moreover, it will be understood that the worker node3306 can use any one any combination of methods to obtain searchconfiguration data from the external data system 12.

In some cases, prior to requesting the search configuration data, theworker nodes 3306 can request the external data systems 12 to return itsversion or some other indication of the functionality of the externaldata system 12. Based on the determined functionality of the externaldata system 12, the worker node 3306 can determine whether it will beable to obtain search configuration data from the external data system12. In certain embodiments, search configuration data received by aworker node 3306 from an external data system 12 is returned to thequery coordinator 3304. In some embodiments, the worker nodes 3306retains the search configuration data as described herein.

At block 5408, the query coordinator 3304 dynamically generates asubquery for the external data systems 12, as described in greaterdetail herein at least with reference to block 4206 of FIG. 42 and block5206 of FIG. 52 . As described, in some embodiments, the querycoordinator 3304 can generate the subquery for the external data system12 based on the search configuration data. For example, the searchconfiguration data can include definitions, instruction sets, or namingconventions specific to the external data system 12. This informationcan be used to further generate a subquery for execution by the externaldata system 12. For example, using the search configuration data thequery coordinator 3304 can transform an initial subquery (e.g., subqueryas found in a query or in an external query configuration file) into anative format for execution by the external data system 12. In this way,the system 16 can reduce the amount of processing to be performed by theexternal data system 12.

In some embodiments, the subquery can include instructions for theexternal data system 12 to communicate the partial results to one ormore worker nodes 3306. As described herein, the partial results can bedistributed amongst multiple worker nodes 3306 in a variety of ways.Furthermore, in some cases, such as when the external data system 12 isa secondary data intake and query system, the subquery can includeinstructions for indexers 206 or worker nodes 3306 of the external datasystem 12 to communicate the partial results to the worker nodes 3306.By instructing the indexers 206 or worker nodes 3306 of the externaldata system 12 to communicate the partial results to the worker nodes3306, the system 16 can avoid a bottleneck at the search head 210 orcontroller of the external data system 12. However it will be understoodthat the subquery can include instructions for partial results fromindexers 206 or worker nodes 3306 of the external data system 12 to becommunicated to the search head 210 of the external data system 12,which can communicate the partial results to the worker nodes 3306. Insome embodiments, the subquery may not include explicit instructions forthe indexers or worker nodes 3306, but may include instructions for thesearch head 210 to generate instructions for the indexers 206 or nodes3306 to communicate the results to the worker nodes 3306.

In certain embodiments, the worker nodes 3306 generate the subqueryusing the search configuration data. For example, a worker node 3306 canuse the subquery received from the query coordinator 3304 and the searchconfiguration data received from the external data system 12 to generatea subquery for execution by the external data system 12. In someembodiments, the worker node 3306 can generate the subquery before,after, or concurrently with the query coordinator 3304 generatinginstructions for the worker node 3306 as will be described herein withreference to block 5410. It will be understood that the worker nodes3306 can generate the subqueries as part of the query processing phase5102 and/or as part of the query execution phase 5104. By generating thesubquery on the worker node 3306, the system 16 can distributeprocessing tasks across various nodes 3306 and reduce the amount ofprocessing performed by the query coordinator 3304. In this way, thesystem 16 can reduce the likelihood of creating a bottleneck at thequery coordinator 3304.

At block 5410, the query coordinator 3304 generates instructions for theworker nodes 3306. As described herein, at least with reference to block5208 of FIG. 52 , the query coordinator 3304 can generate instructionsfor the worker nodes 3306 based on a variety of parameters and caninclude instructions to: distribute subqueries to external data systems12, receive local search identifiers used by the external data systems12 to identify their respective subqueries (and partial results), mapthe local search identifiers for subqueries to corresponding primarysearch identifiers, concurrently receive and process partial resultsfrom multiple external data systems 12 (in some cases based on the localsearch identifier-primary search identifier mapping), distribute partialresults from one multiple external data system 12 to multiple workernodes 3306, combine, and further process results, and communicate searchresults to the query coordinator 3304, etc.

At block 5412, the query coordinator 3304 executes the query asdescribed in greater detail herein at least with reference to block 4010of FIG. 40 and block 5210 of FIG. 52 . It will be understood that fewer,more, or different blocks can be used as part of the routine 5400. Forexample, in some embodiments, the routine 5400 can further include,monitoring nodes 3306 during query execution, allocating/deallocatingresources based on the query, etc. As another example, in certainembodiments, the determination of the data ingest estimate and thepartition size and quantity can form part of a processing query block,similar to the process query block 3804 of FIG. 38 . As yet anotherexample, in some embodiments, block 5406 can be omitted. Instead, thequery coordinator 3304 can generate instructions for the worker node3306 to generate the subquery for the external data system 12 asdescribed herein. Moreover, it will be understood that one or moreblocks described herein with reference to routine 5400 can be combinedwith one or more blocks of other routines described herein, such as theroutines described herein at least with reference to FIGS. 5, 6, 23-26,31, 34, 38-45, 47, 49, 52, 53, 55-57, and 59 .

Furthermore, it will be understood that the various blocks describedherein with reference to FIG. 54 can be implemented in a variety oforders. In some cases, the system 16 can implement some blocksconcurrently or change the order as desired. For example, the system 16can concurrently generate a subquery for the external data system 12(5408) and instructions for the worker nodes 3306 (5410), or in anyorder, as desired. Moreover, in some embodiments, the query coordinator3304 can receive a transformed subquery from the external data system 12and include the transformed subquery in the instructions for the workernode 3306 to execute the query or subquery. In certain cases, the querycoordinator 3304 can further process the transformed subquery based onsearch configuration data received from the external data system 12.

29.0. Distributing Partial Results to Worker Nodes Flow

FIG. 55 is a flow diagram illustrative of an embodiment of a routine5500 implemented by the query coordinator 3304 to execute a query ondata from an external data system 12. Although described as beingimplemented by the query coordinator 3304, it will be understood thatone or more elements outlined for routine 5500 can be implemented by oneor more computing devices/components that are associated with a dataintake and query system 16, such as the search head 210, search processmaster 3302, indexer 206, and/or worker nodes 3306. Thus, the followingillustrative embodiment should not be construed as limiting.

At block 5502, the query coordinator 3304 receives a query, as describedherein at least with reference to block 3802 of FIG. 38 . At block 5504,the query coordinator 3304 identifies an external data system 12, asdescribed in greater detail herein at least with reference to block 3902of FIG. 39 and block 5204 of FIG. 52 .

At block 5506, the query coordinator 3304 dynamically generates asubquery for the external data system 12. As described herein, the querycoordinator 3304 can generate a subquery for the external data system 12based on the determined functionality of the external data system, andcan determine the version or functionality of the external data systems12 in a variety of ways. In some cases, the query coordinator 3304 canobtain location and/or communication information from an external queryconfiguration file that enables the query coordinator 3304 tocommunicate with the external data system 12. Using the obtainedinformation, the query coordinator 3304 can communicate with theexternal data system 12 to determine its functionality.

In certain embodiments, the query coordinator 3304 can map one or moreworker nodes 3306 to different external data systems 12 forcommunication purposes. The query coordinator 3304 can instruct theworker nodes 3306 to obtain information regarding the functionality ofthe external data system 12. Based on the determined functionality ofthe external data system 12, the worker node 3306 can dynamicallygenerate the subquery for execution by the external data system 12. Forexample, the query coordinator 3304 can determine that the external datasystem 12 is capable of communicating its partial results to multipleworker nodes 3306. As such, the query coordinator 3304 can generate asubquery that instructs the external data system 12 to communicate itspartial results to multiple worker nodes 3306 in a distributed manner.

As described herein, in some cases, the query coordinator 3304 cangenerate instructions for indexers 206 and/or worker nodes 3306 of anexternal data system 12 to communicate results to the worker nodes 3306.In certain cases, the query coordinator 3304 can generate instructionsfor the search head 210 of an external data system 12 to communicatepartial results to the worker nodes 3306 or to generate instructions forthe indexers 206 and/or worker nodes 3306 to communicate partial resultsto the worker nodes 3306. In certain embodiments, the instructions cancause the indexers 206, worker nodes 3306 (of the external data system12), and/or search head 210 to communicate the partial results to theworker nodes 3306 without storing the results to disk. For example, theinstructions can cause the search head 210 to stream results receivedfrom the indexers 206 or worker nodes 3306 (of the external data system12) to the worker nodes 3306 prior to, concurrently with, or instead ofstoring the results to disk. However, it will be understood that in somecases, the partial results from the external data system 12 can bestored to disk prior to being communicated to the worker nodes 3306.

Additional details regarding the process of generating a subquery forthe external data systems 12 is described in greater detail herein atleast with reference to block 4206 of FIG. 42 and block 5206 of FIG. 52. For example, as described herein, in some embodiments, the workernodes 3306 can generate a portion or all of a subquery for an externaldata system 12. By generating the subquery on the worker node 3306, thesystem 16 can distribute processing tasks across various nodes 3306 andreduce the amount of processing performed by the query coordinator 3304.

At block 5508, the query coordinator 3304 generates instructions for theworker nodes 3306. As described herein, at least with reference to block5208 of FIG. 52 , the query coordinator 3304 can generate instructionsfor the worker nodes 3306 based on a variety of parameters and caninclude instructions to: distribute subqueries to external data systems12, receive local search identifiers used by the external data systems12 to identify their respective subqueries (and partial results), mapthe local search identifiers for subqueries to corresponding primarysearch identifiers, concurrently receive and process partial resultsfrom multiple external data systems 12 (in some cases based on the localsearch identifier-primary search identifier mapping), distribute partialresults from one multiple external data system 12 to multiple workernodes 3306, combine, and further process results, and communicate searchresults to the query coordinator 3304, etc. In some embodiments, thequery coordinator 3304 generates instructions for the worker nodes 3306based on the functionality and capabilities of the external data system12, the amount of resources allocated for the search, the amount ofprocessing to be performed by the query coordinator 3304, worker nodes3306 and external data system 12, etc.

At block 5510, the query coordinator 3304 executes the query asdescribed in greater detail herein at least with reference to block 4010of FIG. 40 and block 5210 of FIG. 52 . It will be understood that fewer,more, or different blocks can be used as part of the routine 5500. Forexample, in some embodiments, the routine 5500 can further include,monitoring nodes 3306 during query execution, allocating/deallocatingresources based on the query, etc. As another example, in some certainembodiments, the generation of the subquery for the external data system12 can form part of a processing query block, similar to the processquery block 3804 of FIG. 38 . As yet another example, in someembodiments, block 5506 can be omitted. Instead, the query coordinator3304 can generate instructions for the worker node 3306 to generate thesubquery for the external data system 12 as described herein. Moreover,it will be understood that one or more blocks described herein withreference to routine 5500 can be combined with one or more blocks ofother routines described herein, such as the routines described hereinat least with reference to FIGS. 5, 6, 23-26, 31, 34, 38-45, 47, 49,52-54 and 56-58, and 59 .

Furthermore, it will be understood that the various blocks describedherein with reference to FIG. 55 can be implemented in a variety oforders. In some cases, the system 16 can implement some blocksconcurrently or change the order as desired. For example, the system 16can concurrently generate a subquery for the external data system 12(5506) and instructions for the worker nodes 3306 (5508) or in anyorder, as desired.

30.0. Distribution of Partial Results Between Worker Nodes Flow

FIG. 56 is a flow diagram illustrative of an embodiment of a routine5600 implemented by the query coordinator 3304 to execute a query ondata from an external data system 12. Although described as beingimplemented by the query coordinator 3304, it will be understood thatone or more elements outlined for routine 5600 can be implemented by oneor more computing devices/components that are associated with a dataintake and query system 16, such as the search head 210, search processmaster 3302, indexer 206, and/or worker nodes 3306. Thus, the followingillustrative embodiment should not be construed as limiting.

At block 5602, the query coordinator 3304 receives a query, as describedherein at least with reference to block 3802 of FIG. 38 . At block 5604,the query coordinator 3304 identifies an external data system 12, asdescribed in greater detail herein at least with reference to block 3902of FIG. 39 and block 5204 of FIG. 52 .

At block 5606, the query coordinator 3304 dynamically generates asubquery for the external data system 12. As described herein at leastwith reference to block 5506 of FIG. 55 , in some embodiments, the querycoordinator 3304 can determine the functionality or version of theexternal data system 12. Based on the determined functionality of theexternal data system 12, the query coordinator 3304 can dynamicallygenerate a subquery for execution by the external data system 12. Forexample, the query coordinator 3304 can determine that the external datasystem 12 is not capable of communicating its partial results tomultiple worker nodes 3306. As such, the query coordinator 3304 cangenerate a subquery that instructs the external data system 12 tocommunicate its partial results to a single worker node 3306. In somecases, the external data system 12 stores the results to disk and thencommunicates the results from disk to the worker nodes 3306. However, itwill be understood that in some embodiments, the external data system 12can stream the results to the worker nodes 3306 prior to, concurrentlywith, or instead of storing the results to disk.

Additional details regarding the process of generating a subquery forthe external data systems 12 is described in greater detail herein atleast with reference to block 4206 of FIG. 42 and block 5206 of FIG. 52. For example, as described herein, in some embodiments, the workernodes 3306 can generate a portion or all of the subquery for an externaldata system 12. By generating the subquery on the worker node 3306, thesystem 16 can distribute processing tasks across various nodes 3306 andreduce the amount of processing performed by the query coordinator 3304.

At block 5608, the query coordinator 3304 generates instructions for theworker nodes 3306. As described herein, at least with reference to block5208 of FIG. 52 , the query coordinator 3304 can generate instructionsfor the worker nodes 3306 based on a variety of parameters and caninclude instructions to: distribute subqueries to external data systems12, receive local search identifiers used by the external data systems12 to identify their respective subqueries (and partial results), mapthe local search identifiers for subqueries to corresponding primarysearch identifiers, concurrently receive and process partial resultsfrom multiple external data systems 12 (in some cases based on the localsearch identifier-primary search identifier mapping), distribute partialresults from one multiple external data system 12 to multiple workernodes 3306, combine and further process results, and communicate searchresults to the query coordinator 3304, etc.

In some embodiments, the query coordinator 3304 generates instructionsfor the worker nodes 3306 based on the functionality and capabilities ofthe external data system 12, the amount of resources allocated for thesearch, the amount of processing to be performed by the querycoordinator 3304, worker nodes 3306 and external data system 12, etc. Insome embodiments, the instructions for the worker nodes 3306 can includeinstructions for a worker node 3306 assigned to receive partial resultsfrom the external data system 12 to distribute the partial resultsamongst multiple worker nodes 3306. In this way, the results from theexternal data system 12 can be processed in a distributed manner.

At block 5610, the query coordinator 3304 executes the query asdescribed in greater detail herein at least with reference to block 4010of FIG. 40 and block 5210 of FIG. 52 . It will be understood that fewer,more, or different blocks can be used as part of the routine 5600. Forexample, in some embodiments, the routine 5600 can further include,monitoring nodes 3306 during query execution, allocating/deallocatingresources based on the query, etc. As another example, in some certainembodiments, the generation of the subquery for the external data system12 can form part of a processing query block, similar to the processquery block 3804 of FIG. 38 . As yet another example, in someembodiments, block 5606 can be omitted. Instead, the query coordinator3304 can generate instructions for the worker node 3306 to generate thesubquery for the external data system 12 as described herein. Moreover,it will be understood that one or more blocks described herein withreference to routine 5600 can be combined with one or more blocks ofother routines described herein, such as the routines described hereinat least with reference to FIGS. 5, 6, 23-26, 31, 34, 38-45, 47, 49,52-55, 57, and 59 .

Furthermore, it will be understood that the various blocks describedherein with reference to FIG. 56 can be implemented in a variety oforders. In some cases, the system 16 can implement some blocksconcurrently or change the order as desired. For example, the system 16can concurrently generate a subquery for the external data system 12(5606) and instructions for the worker nodes 3306 (5608) or in anyorder, as desired.

31.0. Executing a Query Received from Another System Flow

As described herein, in some cases, a data intake and query system canreceive a query from an external data system 12. For example, asecondary data intake and query system can receive a subquery from aprimary data intake and query system (non-limiting examples: from asearch head, query coordinator 3304, and/or a worker node 3306 of theprimary data intake and query system).

Moreover, in some embodiments, the secondary data intake and querysystem can route partial results of the query that it receives (e.g., asubquery received from a primary data take and system) to worker nodes3306 (or other component) of a primary data intake and query system. Thepartial results can be routed from one or more components of thesecondary data intake and query system to one or more components of theprimary data intake and query system. For example, the partial resultscan be routed from a search head 210, query coordinator 3304, indexers206, or worker nodes 3306 of the secondary data intake and query systemto a search head 210, query coordinator 3304, or worker nodes 3306 ofthe primary data intake and query system. In some cases, the results canbe communicated to the primary data intake and query system withoutpassing through the search head 210 or query coordinator 3304 of thesecondary data intake and query system. In this way, results can becommunicated in a distributed manner without passing through a singlepoint and reducing the likelihood of a bottleneck at the search head 210or query coordinator 3304.

Further, in some cases, the secondary data intake and query system canuse worker nodes 3306 to execute the query that it receives.Accordingly, in some embodiments, worker nodes 3306 of the secondarydata intake and query system are used to execute a subquery of a primarydata intake and query system, and worker nodes 3306 of the primary dataintake and query system are used to execute the query of the primarydata intake and query system (including processing the results of thesubquery).

Moreover, in some cases the secondary data intake and query system andprimary data intake and query system can use the same or similar groupof worker nodes 3306 to execute the query and subquery. Accordingly, incertain embodiments, a worker node 3306 can execute portions of asubquery at the behest of a secondary data intake and query system andexecute portions of the query that corresponds to the subquery at thebehest of the primary data intake and query system.

As anon-limiting example, one worker node 3306 can receive instructionsfrom a query coordinator 3304 of the primary data intake and querysystem to communicate a subquery to a secondary data intake and querysystem and to receive partial results of the subquery from the secondarydata intake and query system. In turn, the same worker node 3306 canreceive instructions from a query coordinator 3304 of the secondary dataintake and query system to execute portions of the subquery on datamanaged by the secondary data intake and query system. Further, the sameworker node 3306 can receive instructions from the query coordinator3304 of the secondary data intake and query system to communicatepartial results of the subquery to a worker node 3306 of the primarydata intake and query system, which in this example can be itself.Moreover, the same worker node 3306 can receive instructions from thequery coordinator 3304 of the primary data intake and query system toprocess the partial results that it receives from the secondary dataintake and query system (the partial results that the worker node 3306determined in accordance with instructions received from querycoordinator 3304 of the secondary data intake and query system). Assuch, in some cases, the same worker node 3306 can process or performmultiple transformations on the same set of data based on instructionsreceived from distinct and independent data intake and query systems.Further, the same worker node 3306 can perform the operations andtransformations without either data intake and query system being awarethat it is the same worker node 3306 performing the operations andtransformations on the set of data identified by both data intake andquery systems.

FIG. 57 is a flow diagram illustrative of an embodiment of a routine5700 implemented by a search head 210 to execute a query received froman external data system 12. Although described as being implemented bythe search head 210, it will be understood that one or more elementsoutlined for routine 5700 can be implemented by one or more computingdevices/components that are associated with a data intake and querysystem 16, such as the query coordinator 3304, search process master3302, indexer 206, and/or worker nodes 3306. For example, depending onthe architecture of the data intake and query system 16, portions or allof the routine 5700 can be implemented by a component of the data intakeand query system other than the search head 210. Thus, the followingillustrative embodiment should not be construed as limiting.

At block 5702, the search head 210 receives a query, as described ingreater detail at least with reference to block 602 of FIG. 6 , block3002 of FIG. 30 , and block 3802 of FIG. 38 . At block 5704, the searchhead 210 processes the query as described in greater detail herein atleast with reference to block 604 of FIG. 6 , blocks 3004 and 3006 ofFIG. 30 , and block 3804 of FIG. 38 . As will be understood, the mannerin which the search head 210 (or query coordinator 3304) processes thequery can be based on the architecture of the data intake and querysystem (e.g., whether the architecture includes worker nodes 3306,whether the architecture is cloud based or on premises, etc.). Forexample, as described herein, the search head 210 can generateinstructions for indexers 206 to execute portions of the query and/orgenerate instructions for worker nodes 3306 that have been allocated forthe search to execute portions of the query.

At block 5706, the search head 210 initiates execution of the query. Insome embodiments, initiating execution can include distributing at leasta portion of the query for execution as described herein at least withreference to block 606 of FIG. 6 and block 3806 of FIG. 38 . Forexample, the search head 210 can distribute portions of the query, suchas instructions or subqueries, to indexers 206 and/or worker nodes 3306for execution.

At block 5708, the search head 210 receives results. In someembodiments, the search head 210 receives results from indexers 206 asdescribed herein at least with reference to block 610 of FIG. 6 . Incertain embodiments, the search head 210 can receive results from workernodes 3306 as described herein at least with reference to block 3012 ofFIG. 30 or block 3808 of FIG. 38 . Furthermore, the search head 210 canperform additional processing on the received results as describedherein at least with reference to block 610 of FIG. 6 and block 3810 ofFIG. 38 .

At block 5710, the search head 210 provides the results to another dataintake and query system. For example, the search head 210 can providethe results to a search head 210, query coordinator 3304, and/or one ormore worker nodes 3306 of a primary data intake and query system. Insome cases, the search head 210 stores the results to disk andcommunicates the results from disk to the data intake and query system.In certain cases, the search head 210 can stream the results to theother data intake and query system prior to, concurrently with, orinstead of storing the results to disk.

As described herein, the primary data intake and query system canfurther process the results received from the search head 210. Further,the results from the search head 210 can correspond to partial resultsof a query received by the primary data intake and query system.Accordingly, the query executed by the data intake and query system cancorrespond to a subquery of a query received by a primary data intakeand query system.

It will be understood that fewer, more, or different blocks can be usedas part of the routine 5700. For example, in some embodiments, resultsof the query can be provided to the primary data intake and query systemfrom the indexers and/or worker nodes 3306. In such embodiments, block5708 may be omitted as the search head may not receive the results (andblock 5710 may be performed by the indexers 206 and/or worker nodes3306). Moreover, it will be understood that one or more blocks describedherein with reference to routine 5700 can be combined with one or moreblocks of other routines described herein, such as the routinesdescribed herein at least with reference to FIGS. 5, 6, 23-26, 31, 34,38-45, 47, 49, 52-56, and 59 .

Furthermore, it will be understood that the various blocks describedherein with reference to FIG. 57 can be implemented in a variety oforders. In some cases, the system 16 can implement some blocksconcurrently or change the order as desired.

32.0. Task Distribution within an Execution Node

An execution node in a distributed execution environment, such as, butnot limited to a worker node 14, can receive and process data frommultiple datasets. The datasets may correspond to data from differentdata sources, such as datasets from different external data systems 12or different data intake and query systems, data associated withdifferent DAGs, and/or different datasets from the same data source. Forexample, a query can include instructions to obtain different sets ofdata from the same (or different) data source, independently process thedifferent sets of data, and combine the processed sets of differentdata, and process the combined set of data. In some embodiments, thedifferent datasets or the processing of the different datasets cancorrespond to sub-DAGs of a larger DAG being executed by the executionnode.

In some cases, an execution node may begin to process data from onedataset while ignoring data from another dataset. In doing so, theexecution node can cause the query or subquery to fail. As anon-limiting example, data from different datasets can be sent to one ormore buffers of the execution node. As the execution node processes thedata, it can remove the data being processed from the buffer and free upadditional space for additional data. However, if the execution nodeonly processes data from one dataset, data from the other datasets willnot be removed and associated buffers can fill up.

Once a buffer at the execution node is full, the execution node mayreject incoming data or incoming data associated with datasets that arenot being processed. In response, buffers at the data source used forsending data to the execution node may also fill up as the data is nolonger being sent to the execution node. As the buffers at the datasource fill up or after a predetermined amount of time in which data isnot accepted by the execution node, the data source may determine thatthe execution node is not functioning or that there is some other issueassociated with the execution node. As such, the data source may producean error, stop sending results to the execution node, and/or cancel acorresponding query or subquery.

To address this issue, the execution node can be configured toconcurrently process data from different datasets. FIG. 58 is a blockdiagram illustrating an embodiment of a data path of data from differentdata sources 5802 in an execution node 5804. Non-limiting examples ofexecution nodes 5804 are described herein at least with reference toworker nodes 14. In some embodiments, the data sources 5802 cancorrespond to any source of data that is to be processed by theexecution node 5804. For example, the data source 5802 can correspond toanother execution node 5804, indexers 206, external data sources 3318,the query acceleration data store 3308, common storage 4602, an ingesteddata buffer 4802, a search head 210, and may logically correspond todifferent DAGs or sub-DAGs of the same DAG, etc.

In the illustrated embodiment, chunks of data or data chunks 5806 fromdifferent data sources (or corresponding to different datasets) 5802 arecommunicated to the execution node 5804. Each data chunk 5806 caninclude records, events, or data that is to be processed by theexecution node 5804. For example, a data chunk 5806 can include one ormore events or records that correspond to partial results received froma secondary data intake and query system.

In some embodiments, the data chunks 5806 received by the execution node5804 are placed in an intake buffer 5808. In the illustrated embodiment,the data chunks 5806 in the intake buffer 5808 include two data chunks5806 from a first data source (each labeled “S1 Data Chunk”), two datachunks 5806 from a second data source (each labeled “S2 Data Chunk”),and one data chunk 5806 from a third data source (each labeled “S3 DataChunk”). The data chunks 5806 in the intake buffer 5808 may correspondto partial or complete chunks of data received from the data sources5802. Further, the data chunks 5806 in the intake buffer 5808 can remainin the intake buffer 5808 until the entire chunk of data has beenreceived from the data source 5802.

Once the data chunk 5806 is complete it can be moved to the data chunkbuffer 5810. In some embodiments, the execution node 5804 can determinethat the data chunk 5806 is complete based on an identification of adata source identifier within the data chunk 5806. For example, eachchunk of data 5806 received by the execution node 5804 can include anidentifier indicating the source of the data chunk 5806. In this way,the execution node 5804 can track the different data chunks 5806 to beprocessed. In some embodiments, the data source identifier cancorrespond to the local search identifier assigned by a secondary dataintake and query system.

To concurrently process data chunks 5806 in the data chunk buffer 5810,the execution node 5804 can use one or more partition generators 5812.In some embodiments, the execution node 5804 can include a distinctpartition 5812 generator for data chunks 5806 from each data source5802. For example, in the illustrated embodiment, the execution node5804 receives data chunks 5806 from three data sources 5802. As such,the execution node 5804 can include three partition generators 5812.However, it will be understood that fewer or more partition generators5812 can be used by the execution node 5804 to process data chunks fromdifferent data sources as desired. As a non-limiting example and withreference to the illustrated embodiment, one partition generator 5812(labeled “S1 Partition Generator”) can generate partitions 5816 (labeled“S1 Partitions”) for the partition queue 5814 by combining S1 datachunks from the data chunk buffer 5810. Similarly, two other partitiongenerators 5812 (labeled “S2 Partition Generator” and “S3 PartitionGenerator”) can generate partitions 5816 (labeled “S2 Partitions” and“S3 Partitions”, respectively) by combining S2 data chunks and S3 datachunks, respectively, from the data chunk buffer 5810.

Moreover, each partition generator 5812 can identify data chunks 5806 tobe combined based on the data source identifiers. In some embodiments,such as where the execution node 5804 is to combine data chunks 5806associated with partial results, the partition generators 5812 can usethe primary search identifier, local search identifier, or mappingbetween the primary and local search identifier to identify data chunks5806 to be combined to form a partition 5816. For example, the partitiongenerator 5812 may receive instructions to combine data chunks 5806 thathave the same primary search identifier into a partition 5816. However,the data chunks 5806 in the data chunk buffer 5810 may not have aprimary search identifier included therewith. As such, the partitiongenerator 5812 can map the primary search identifier to the local searchidentifier in order to identify the data chunks 5806 that are to becombined.

As described herein, the size of each partition 5816 or number ofrecords placed therein can be based on resources allocated to theexecution node 5804 or search. For example, the size of the partitions5816 can be determined based on the number of processors 5818 and/oramount of memory allocated to the execution node 5804 or search and/orthe size of each record. In some embodiments, the partition size can beselected to avoid having the amount of data to be processed by theexecution node 5804 exceeding the amount of volatile memory available tothe execution node 5804, which may also be referred to spilling data todisk.

In addition to combining multiple data chunks 5806 to form a partition5816, a partition generator 5812 can add execution instructions to eachpartition 5816. The instructions can indicate what transformation orprocesses are to be performed on the data of the partition 5816(non-limiting examples: events or records that made up the data chunks5806 used to form the partition 5816). In some embodiments, theinstructions can be in the form of binary code executable by aprocessor. The partition generators 5812 can obtain the instructions forthe partition based on the instructions received by the execution node5804. For example, the instructions generated by a query coordinator3304 and communicated to an execution node 5804 can include theinstructions for processing individual partitions 5816. It will beunderstood that the instructions for each partition 5816 can varydepending on the transformation that is to be performed on the data ofthe partition 5816 or dataset.

The partitions 5816 in the partition queue 5814 can be scheduled forprocessing by aprocessor 5818 of the execution node 5804. Further, thedata of the partition 5816 can be processed by the processor 5818 of theexecution node 5804 according to the instructions included in thepartition 5816. As mentioned, in certain embodiments, the partitions5816 can be scheduled and processed without regard to the sourceidentifier used to create the partition 5816. In this way, the executionnode 5804 can concurrently process data from different data sources5802.

In some embodiments, multiple execution nodes 5804 can communicate witheach other to distribute partitions or tasks for execution. For example,if the partition queue 5814 in one execution node 5804 satisfies a queuethreshold, it can communicate with other execution nodes 5804 to sendpartitions to them for execution. In some cases, the queue threshold canbe based on a predetermined number or can be dynamically determinedbased on the partition queue sizes of other worker nodes 3306 or othermeans. For example, the queue threshold can be satisfied if the numberof partitions 5816 in the partition queue 5814 of one execution node5804 is 50% (or some other amount) greater than the number of partitionsin the partition queue 5814 of another execution node 5804.

As described herein, in some embodiments, an execution node controller,such as the query coordinator 3304, can monitor the execution nodes5804. If one execution node 5804 is falling behind or satisfies a queuethreshold or timing threshold (non-limiting example, is taking longerthan an expected time to execute its portion of the query), theexecution node controller can instruct the execution node 5804 todistribute some of its partitions 5816 or data chunks 5806 to anotherexecution node 5804 for execution. Similarly, if one execution node 5804has significantly fewer or no partitions to execute, the querycoordinator can instruct other execution node 5804 to distribute some oftheir partitions 5814 or data chunks 5806 to the other execution node5804 for execution.

In addition, the execution node controller can monitor the number oramount of data chunks 5806 assigned to a worker node 3306. For example,based on the distribution of data from data sources 5802 to worker nodes3306, it is possible that one execution node 5804 receives asignificantly larger portion of data to process than other executionnode 5804 (non-limiting example: similar to the queue threshold, thenumber of data chunks in the intake buffer or data chunk buffer satisfya buffer threshold). In such cases, the execution node controller caninstruct the execution nodes 5804 to redistribute their data chunks 5806or partitions 5816 in order to process the data in a more distributedfashion thereby decreasing the search execution time. Moreover, in somecases, the execution node controller can instruct the data sources 5802to distribute their data in a different way to reduce the likelihood ofsending too much data to a single execution node 5804.

Although described often with reference to components of a data intakeand query system, it will be understood that the functions anddescriptions described herein with reference to the execution node 5804can be used in a variety of distributed execution environments.

32.1. Worker Node Task Distribution Flow

FIG. 59 is a flow diagram illustrative of an embodiment of a routine5900 implemented by an execution node 5804 to process a partition ortask. Although described as being implemented by the execution node5804, it will be understood that one or more elements outlined forroutine 5900 can be implemented by one or more computingdevices/components in a distributed execution environment, such as, butnot limited to one or more components of a data intake and query system16, such as the worker node 3306, search head 210, search process master3302, indexer 206, and/or query coordinator 3304. Thus, the followingillustrative embodiment should not be construed as limiting.

At block 5902, the execution node 5804 receives chunks of data. Asdescribed herein, the chunks of data can be received over time and caninclude one or more records or events. As such, partial data chunks canbe maintained by the execution node 5804 in an intake buffer. Further,as described herein, the data chunks can be received from different datasources and/or be associated with different datasets. The differentdatasets can correspond to external data systems 12, data intake andquery systems, sub DAGs of a larger DAG, or different sets of data fromthe same data source, etc.

At block 5904, the execution node 5804 generates a task or partition. Insome embodiments, the execution node 5804 can generate the partition bycombining multiple chunks of data. As described herein, the size of eachpartition or number of records placed therein can be based on resourcesallocated to the execution node 5804. In some cases, the execution node5804 combines data chunks associated with the same dataset into thepartition. For example, data chunks associated with or received from afirst data source can be combined to form one partition and data chunksassociated with or received from a second data source can be combined toform a different partition. Similarly, data chunks associated with afirst DAG or sub-DAG can be combined to form one partition and datachunks associated with a second DAG or sub-DAG can be combined to form adifferent partition.

In certain embodiments, the execution node 5804 identifies data chunksassociated with the same dataset based on a data source identifierassociated with each data chunk. As described herein, in some cases, theexecution node 5804 can perform a mapping function to identify relateddata chunks. For example, the execution node 5804 may receive anindication that data chunks with a particular primary search identifierare to be combined, and use a primary-local search identifier mapping toidentify data chunks with a corresponding local search identifier forcombination.

Moreover, as part of generating a partition, the execution node 5804 canadd computer executable instructions to the combined data chunks. Theadded instructions can indicate what is to be done to the data orrecords of the partition. For example, the instructions can indicate oneor more transformations to be performed on the records, such as afiltering or joining of records. In some embodiments, the execution node5804 can receive the instructions from an execution node controller,such as, but not limited to a query coordinator 3304 of a data intakeand query system.

In some embodiments, such as where the execution node 5804 processesdata received from a secondary data intake and query system according toinstructions received by a primary data intake and query system, theexecution node 5804 can determine what instructions are to be includedfor each partition based on an association between or mapping of aprimary search identifier associated with the primary data intake andquery system with a local search identifier associated with thesecondary data intake and query system. For example, as describedherein, when generating instructions for the execution node 5804, theprimary data intake and query system may not know the identifier thatwill be applied to data chunks or partial results from a secondary dataintake and query system. As such, the primary data intake and querysystem can assign a primary search identifier for data chunks or partialresults that it expects to receive from a particular secondary dataintake and query system. As the secondary data intake and query systemprocesses the data according to the query or subquery, it can append orinclude a local search identifier to or with each chunk of data. Thus,the association or mapping can enable the execution node 5804 todetermine what is to be done (using the primary search identifier) todata chunks having a particular local search identifier.

At block 5906, the execution node 5804 schedules the partitions forexecution by one or more processors of the execution node 5804. Thepartitions can be scheduled for execution in a variety of ways. Forexample, the partitions can be executed in a random order, in atime-based order (e.g., first-in first out), etc. In certainembodiments, the partitions are executed without regard to the datasource identifier associated therewith. That is, the processors cantreat partitions associated with different data sources equally suchthat partitions associated with one data source are not always processedbefore partitions associated with a different data source.

At 5908, the execution node 5804 processes the partition. As describedherein, the execution node 5804 can process the partitions based on theexecutable instructions in the partition. It will be understood thatfewer, more, or different blocks can be used as part of the routine5900. For example, in some embodiments, executable instructions may notbe included in each partition or task. In such embodiments, theexecution node 5804 can retrieve instructions for a particularpartition. In some cases, the execution node 5804 can retrieve theinstructions based on the primary or local search identifier, orinstructions received from a controller, such as a query coordinator3304, etc.

As another example, in some embodiments, an execution node can processone partition based on instructions received from one execution nodecontroller and then process the results of processing the partitionbased on instructions received from another execution node controller.For example, a secondary data intake and query system may use anexecution node to process a subquery. In processing the subquery, theexecution node can generate and process partitions according toinstructions received from the secondary data intake and query system.Further, a primary data intake and query system may use the executionnode to process the partial results of the subquery as part of afederated or multi-system query. Accordingly, the execution node can,according to instructions received from the primary data intake andquery system, generate and/or process a second partition that includesthe results that it generated from processing an earlier partition onbehalf of the second data intake and query system. It will be understoodthat the second partition can include results from the execution ofother partitions by the worker node or by other execution nodes.

Moreover, it will be understood that one or more blocks described hereinwith reference to routine 5900 can be combined with one or more blocksof other routines described herein, such as the routines describedherein at least with reference to FIGS. 5, 6, 23-26, 31, 34, 38-45, 47,49, and 52-57 . Furthermore, it will be understood that the variousblocks described herein with reference to FIG. 59 can be implemented ina variety of orders.

33.0. Federated Search Optimization

As previously described, in some embodiments, it can be beneficial toperform queries across multiple data systems, such as the data intakeand query system 16 and the external data systems 12. In someembodiments, an external data system 12 may support a different languagethan the data intake and query system 16. The language of the dataintake and query system 16 and of the external data systems 12 may referto the vocabulary, syntax, and/or grammatical rules used to instruct thesystems, including to request searches and queries. In some cases, thelanguage may refer to a query language. In one non-limiting example, thedata intake and query system 16 may support or be able to executequeries written in SPL and the external data system may support or beable execute queries written in Lucene or SQL. Further, the data intakeand query system 16 may, in some cases, not support or be able toexecute queries written in Lucene or SQL and the external data system 12may, in some cases, not support or be able to execute queries written inSPL.

Because the data intake and query system 16 and the external data system12 may support different query languages, a query that is performedacross both the data intake and query system 16 and the external datasystem 12 may be written in multiple query languages. For example, aquery provided to or generated by the data intake and query system 16may be written in SPL, and may include or reference a portion orsubquery that is written in another language, such as SQL or JSON. Insome cases, the query may be suboptimal because the subquery is writtenin a language not supported by the system that received or generated thequery. Accordingly, in some cases, it may not be possible to applynative optimization features of the data intake and query system 16 tothe subquery that may be written in a different language than thatsupported by the data intake and query system 16.

Embodiments disclosed herein include a system that can convert ortranslate a query or subquery from an unsupported query language to asupported query language. Once the query is converted, the system canapply its native optimization capabilities to the converted ortranslated query or subquery. The optimized query or subquery can thenbe converted or translated back to its original query language enablingthe optimized translated query to be executed by an external data systemthat supports the original language of the query. Advantageously, incertain embodiments, by converting and optimizing the query, thecomputing resources for executing the query can be reduced. In someembodiments, the amount of processing nodes (e.g., worker nodes), theamount of bandwidth, the amount of compute time, and/or the amount ofstorage space required to execute a query may be reduced by convertingand optimizing the query before execution of the query.

FIG. 60 is a flow diagram illustrative of an embodiment of a routineimplemented 6000 by a query coordinator 3304 to optimize and execute aquery involving data from an external data system 12. The external datasystem 12 may include a third-party data processing and storage system5000, which may support a different language or query language than adata intake and query system 16. Although described as being implementedby the query coordinator 3304, it will be understood that one or moreelements outlined for routine 6000 can be implemented by one or morecomputing devices/components that are associated with a data intake andquery system 16, such as the search head 210, search process master3302, indexer 206, and/or worker nodes 3306. Thus, the followingillustrative embodiment should not be construed as limiting.

At block 6002, the query coordinator 3304 receives a query, as describedherein at least with reference to block 3802 of FIG. 38 . In certainembodiments, the query may include a number of parts. At least some ofthe parts of the received query may themselves be queries. Theseadditional queries that are part of the query may be referred to as“subqueries.” The query coordinator 3304 may use the result of orresponse to one or more subqueries to help generate a result or responseto the query. In some embodiments, one or more of the subqueries mayreference different data and/or different external data systems 12 thanother subqueries or portions of the query. Each of the queries and/orsubqueries may reference particular data to be processed and a manner ofprocessing the data. For example, the queries may identify data fieldsto be accessed and whether to count, modify, delete, or provide the datato another portion of the query, and the like.

In some embodiments, a subquery may be written in a different languageor query language than a remainder of the query or than othersubqueries. For example, the query may be written in a languageinterpretable by the data intake and query system 16 (e.g., SPL), butthe subquery may be written in a different language that isinterpretable by the external data system 12 (e.g., SQL or JSON). Thelanguage interpretable by the data intake and query system 16 may not beinterpretable by the external data system 12. Similarly, the languageinterpretable by the external data system 12 may not be interpretable bythe data intake and query system 16.

In some embodiments, the subquery or at least a portion of the subquerymay not be directly included in the query. Instead, in certainembodiments, the query may include an identifier or reference thatindicates to the query coordinator 3304 that the query includes asubquery. The query coordinator 3304 may use the identifier or referenceto determine the subquery. For example, the query coordinator 3304 mayuse the reference as an index to an external query configuration file,which may store one or more potential subqueries that may be referencedby a query.

At block 6004, the query coordinator 3304 identifies a subquery for anexternal data system 12. The query coordinator 3304 can identify thesubquery based on a keyword or reference included in the query. Forexample, a keyword, such as “federated” or “external,” may indicate thatwhat follows references an external data system 12. As described above,the query may directly include the subquery to the external data system12 or may include a reference that enables the query coordinator 3304 todetermine the subquery from another location, such as an external queryconfiguration file or other mapping that maps a keyword or reference toa subquery. For instance, the query may include, among other commands,the following: federated:my_dep_3_search_5. The query coordinator 3304may determine from the term “federated” that what follows the ‘:’ (e.g.,my_dep_3_search_5) is a reference to a subquery for querying an externaldata system 12. The query coordinator 3304 may use the my_dep_3_search_5as a reference or index to access a mapping or external queryconfiguration file to determine the actual subquery and/or theparticular external data system 12 to perform the subquery. In addition,the query coordinator 3304 may determine from the subquery and/or fromthe mapping or external query configuration file, the query language inwhich the subquery is written. For example, the query coordinator 3304may determine that the subquery is written in SQL rather than the SPL inwhich the remainder of the query may be written.

At block 6006, the query coordinator 3304 translates, or otherwiseconverts, the subquery into a query language supported by the dataintake and query system 16. As previously described, the query may be ina first query language, such as SPL, and the subquery may be in a secondquery language, such as SQL or JSON. The query coordinator 3304 maytranslate the subquery from second query language (e.g., SQL or JSON) tothe first query language (e.g., SPL). The query coordinator 3304 maydetermine the translation of the subquery from the second query languageto the first query language based on a language mapping that mapscommands and command arguments or variables from the second querylanguage to the first query language.

In some embodiments, a direct mapping between commands may not bepossible. For example, the second query language may have a built-incommand that is not available in the first query language. In some suchcases, the query coordinator 3304 may determine a combination ofcommands or actions in the first query language that accomplishes theresult of the built-in command in the second query language. The querycoordinator 3304 may then replace the built-in command in the secondquery language with the combination of commands in the first querylanguage.

In some embodiments, the query coordinator 3304 may use natural languageprocessing and/or a machine learning process to determine a translationbetween a command in the second query language and a command in thefirst query language. The machine learning process may be a supervisedor unsupervised machine learning process. Further, the machine learningprocess may use a set of test data to develop or refine translationsbetween commands in the first and second query language.

At block 6008, the query coordinator 3304 processes the translatedsubquery. In some embodiments, as part of processing the translatedsubquery, the query coordinator can perform one or more optimizations onthe translated subquery. The query coordinator 3304 may use one or moredifferent optimizing processes to optimize the translated subquery. Forexample, the query coordinator 3304 may use one or more of semantic,runtime, or infrastructure based optimizations. In certain embodiments,the query coordinator 3304 optimizes the translated subquery itself.Alternatively, or in addition, the query coordinator 3304 may optimizethe distribution or assignment of the translated subquery among one ormore worker nodes 3306. Furthermore, as described herein, in someembodiments, the query coordinator 3304 can include instructions tosends results to one worker node 3306 for distribution to other workernodes 3306 or include instructions to distribute results to multipleworker nodes 3306.

In some embodiments, the query coordinator 3304 may optimize the queryas a whole, or portions of the query, based at least in part on thetranslated subquery and/or on an optimization of the translatedsubquery. For example, the query coordinator 3304 may determine based onthe translated subquery that another portion of the query may bemodified or omitted. As another example, the query coordinator 3304 maydetermine that a portion of the translated subquery can be modified oromitted based on another portion of the query. In certain embodiments,translating the subquery to the same query language as the remainder ofthe query (e.g., the query language supported by the data intake andquery system 16) may enable the query coordinator 3304 to recognize oneor more optimizations that may be made to the query (or subquery).

In certain embodiments, the semantic optimization of the translatedsubquery may be an optimization based on the content of the translatedsubquery itself. The query coordinator 3304 may optimize the translatedsubquery by identifying superfluous portions of the subquery oralternative commands that the worker nodes 3306 or the external datasystem 12 may use to achieve the same result as the translated subquery.For example, a subquery may request data from two fields of a table atthe external data system 12. However, if only the data from one field isrequired or used by the query, the query coordinator 3304 may modify thesubquery to remove the request for data from the unused field. Asanother example, if the subquery includes a plurality of commands thatcan be replaced with a single command to achieve the same result, thequery coordinator 3304 may modify the subquery to replace the pluralityof commands with the single command.

In some embodiments, the query coordinator 3304 may apply the semanticoptimization to the entire query. Moreover, in some cases, the querycoordinator 3304 may optimize the query based on the translatedsubquery. For example, the query coordinator 3304 may determine that aportion of the query is unnecessary because, for example, the translatedsubquery provides the requested data or the translated subquery can beoptimized to make the result of the portion of the query unnecessary. Asa more concrete example, suppose that the query coordinator 3304receives a query to obtain a count of all flights across the country andthat the original subquery returns a flight number and flight time foreach flight at an airport within the country. Further, suppose that thequery includes a command to count each flight from each airport and toadd the counts. If the external data system 12 is capable of providingevent count or a count of the number of flights, the query coordinator3304 may determine that the original subquery can be replaced with acount command. By replacing the original subquery with a count command,the amount of processing and bandwidth required for the subquery may bereduced. Further, the query coordinator 3304 may determine that thecount command included in the query is unnecessary because the subquerywas modified to directly obtain the count from the external data system12. Thus, in this particular example, the query coordinator 3304 mayoptimize the query, based at least in part on the subquery, to removethe count command from the query resulting in additional processingimprovements. In some embodiments, the query coordinator 3304 may removethe count command from the query as part of a runtime optimization.

In certain embodiments, the query coordinator 3304 may parse the syntaxof the translated subquery, and/or the query, into a set of componentsor constituent parts based at least in part on the syntax and grammar ofthe query language of the query. The query coordinator 3304 may performsemantic optimization by modifying or replacing one or more of thecomponents or constituent parts of the translated subquery or query.

In certain embodiments, the query coordinator 3304 may perform runtimeoptimization of the subquery or query. Runtime optimization of thetranslated subquery or of the query may include modifying the subqueryor the query upon receipt or execution of at least a portion of thequery based on the results or anticipated results of the query. Thequery coordinator 3304 may determine a result or predict an expectedresult of a portion of the query or the subquery. Based on thedetermined or predicted result, the query coordinator 3304 may determinewhether another portion of the query or subquery can be eliminated ormodified. For example, suppose that a query has a pair of subqueriesthat reference two different external data systems 12, respectively. Thequery coordinator 3304 may determine that the second subquery can bereduced or modified based on the result or predicted result of executingthe first subquery. For instance, the query coordinator 3304 maydetermine that a portion of the second subquery is redundant or isunnecessary based on the data obtained from the first subquery. As such,the query coordinator 3304 may modify the second subquery. As a moreconcrete example, suppose a user is attempting to obtain server trafficdata relating to airports without flight curfews. In this particularexample, the query coordinator 3304 may determine that a first externaldata system 12 can return the identity of airports with flight curfewsin response to a first subquery. As such, in this particular example,the query coordinator 3304 may modify a second subquery that initiallyprovided flight information for all airports to only obtain flightinformation for flights to or from airports without flight curfews.

In certain embodiments, the query coordinator 3304 performsinfrastructure or infrastructure execution optimizations. Infrastructureoptimizations can include any optimizations relating to the number ofworker nodes 3306 and the distribution of the query or portions of thequery among the number of worker nodes 3306. In certain embodiments, thequery coordinator 3304 analyzes the subquery or the query to determinecharacteristics or metadata of the query or subquery. The querycharacteristics or metadata may relate to an expected amount of data tobe obtained in response to executing the subquery or various portions ofthe query. Further, the query characteristics or metadata may includeinformation relating to relationships between different portions of thequery. For example, the query metadata may indicate portions of thequery that are reliant on the results of other portions of the query.

In addition, the query coordinator 3304 may determine one or moreparticular execution objectives for the query. In some cases, theexecution objectives may be received from a user (e.g., anadministrator). In other cases, the execution objectives may bepredefined or determined based on default values. The executionobjectives may include any objectives that may relate to the resourcesused to execute the query. For example, the execution objectives mayrelate to time, number of processors (or worker nodes 3306), bandwidth,memory, or any other computing resource that can be improved by amodification in a query or the distribution of a query among computingresources.

Based on the execution objectives, the query characteristics ormetadata, and/or available computing resources (e.g., available workernodes 3306 or available processor cores at the worker nodes 3306) thequery coordinator 3304 may optimize the scheduling or assignment of thequery. For instance, suppose the execution objective is to reducebandwidth usage. If the query coordinator 3304 assigns a worker node3306 or set of worker nodes 3306 a first portion of a query that servesas an input to a second portion of the query, the query coordinator 3304may assign the second portion of the query to the same worker node 3306or set of worker nodes 3306 to eliminate the need to communicate thedata between different worker nodes resulting in reduced bandwidthrequirements. Alternatively, if the execution objective is to reduceexecution time, the query coordinator 3304 may assign additional workernodes to different portions of the query despite the increased workernodes causing, in some cases, increased bandwidth utilization due toincreased cross-worker node 3306 communication. In certain embodiments,the query coordinator 3304 performs the infrastructure optimization aspart of the block 6012 described below.

At block 6010, the query coordinator 3304 translates the processed queryinto a query language supported by the external data system 12.Translating the processed query may include translating the subquerythat is to be executed by the external data system 12 while maintainingother portions of the query in the query language supported by the dataintake and query system 16. Usually, the query coordinator 3304translates the translated subquery into the query language in which thesubquery identified at the block 6004 was originally written. However,in some embodiments, the translated subquery may be translated to adifferent query language. For example, if it is determined during theoptimization process that a different external data system 12 may moreefficiently perform the query, the query coordinator 3304 may translatethe subquery into the query language supported by the external datasystem 12 identified during the optimization process. In certainembodiments, the block 6010 may include one or more of the embodimentsdescribed with respect to the block 6006.

At block 6012, the query coordinator generates instructions for theworker nodes 3306. Generating instructions for the worker nodes caninclude generating instructions for the worker nodes 3306 to execute themodified subquery and/or the optimized query. In some embodiments,generating the instructions for the worker nodes 3306 may includeperforming infrastructure optimization as described above. In certainembodiments, the block 6012 may include one or more of the embodimentspreviously described with respect to the block 5208 of FIG. 52 .

At block 6014, the query coordinator 3304 executes the query. Executingthe query may include executing the subquery at the external data system12. In certain embodiments, the block 6014 may include one or more ofthe embodiments previously described with respect to the block 5210 ofFIG. 52 .

Advantageously, in certain embodiments, the translation of a subqueryfrom its original language to one supported by the data intake and querysystem 16 for optimization and then back to the original languageenables the use of computing resources of the system 100 to be reduced.In some example embodiments, the query coordinator 3304 may translate asubquery from SQL or JSON to SPL. The query coordinator 3304 may thenprocess and/or optimize the subquery in SPL to obtained a processedsubquery. Alternatively, or in addition, the query coordinator 3304 mayprocess and/or optimize the query that includes the subquery. Theprocessed subquery may then be translated back from SPL to SQL or JSONenabling the processed subquery to be executed at the external datasystem 12. In certain embodiments, the subquery is not optimized ormodified, but the translation of the subquery enables other portions ofthe query to be optimized. In embodiments where the subquery is notmodified or optimized, the subquery may be translated back to itsoriginal language without change. Alternatively, the translated subqueryis discarded and the original subquery is used or re-inserted into themodified query.

As described herein, in some embodiments, the external data systems 12can process and execute the subquery similar to the manner in which thedata intake and query system 16 processes and executes the query.Further, the external data systems 12 can process and execute thesubquery similar to the manner in which it executes other queriesreceived from a user or client device, except that results arecommunicated to one or more worker nodes 3306 of the data intake andquery system 16 instead of (or in addition) to a user or client device.In some embodiments, as part of executing the subquery, the externaldata system 12 can assign the subquery a local search identifier andcommunicate the local search identifier to the worker node 3306. Theworker node 3306 can map the local search identifier with the primarysearch identifier received from the data intake and query system todetermine how the partial results from the external data system 12 areto be processed according to the instructions received from the dataintake and query system 16.

It will be understood that fewer, more, or different blocks can be usedas part of the routine 6000. For example, in some embodiments, theroutine 6000 can further include, monitoring nodes 3306 during queryexecution, allocating/deallocating resources based on the query, etc.Moreover, it will be understood that one or more blocks described hereinwith reference to routine 6000 can be combined with one or more blocksof other routines described herein, such as the routines describedherein at least with reference to FIGS. 5, 6, 23-26, 31, 34, 38-45, 47,49, 52-57, and 59 .

Furthermore, it will be understood that the various blocks describedherein with reference to FIG. 60 can be implemented in a variety oforders. In some cases, the system 16 can implement some blocksconcurrently or change the order as desired. For example, the system 16can concurrently optimize the query and generate instructions for workernodes. In particular, the system 16 can perform infrastructureoptimization while generating instructions for worker nodes, or in anyorder, as desired. In some cases, the results from the query of datawithin the data intake and query system 16 can become linked with thepartial results received from the external data systems 12.

34.0 Subquery Configuration File

FIG. 61 illustrates an example of an external query configuration file6100 in accordance with disclosed embodiments. As previously described,in a number of embodiments, a configuration file, such as theconfiguration file 6100 may identify one or more queries or subqueriesthat can be performed at one or more external data systems 12. In someembodiments, the data intake and query system 16 may be associated withone or more configuration files 6100.

In the illustrated embodiment, the external query configuration file6100 includes a deployment portion 6102 and a query portion 6104.However, it will be understood that the external query configurationfile 6100 can include fewer or more portions as desired. For example,the deployment portion 6102 and query portion 6104 can be combinedand/or one or more entries of the deployment portion 6102 and/or queryportion 6104 can be stored as one or more entries of a database, suchas, a relational database like dynamoDB or Aurora DB, etc.

The query portion 6104 may include one or more queries or subqueriesthat can be referenced within a query. Each of the queries may include areference or keyword that identifies the query within the external queryconfiguration file 6100. In some cases, a user or client system canprovide a query to the data intake and query system 16 that incorporatesone or more of the references identifying one or more of the subqueries.For example, the query may include: “federated:my_dep_1_search_1” aspart of the query indicating that the query should incorporate the firstsubquery in the query portion 6104.

In addition, the query portion 6104 can include additional informationassociated with the subqueries, such as, but not limited to, anidentification of the external data system 12 to execute the query, aquery type, estimated number of results received from the external datasystem 12 based on the query, the number of fields of the resultsreceived, or other information associated with the query, etc.

In the illustrated embodiment, the query portion 6104 includes fourentries associated with four different subqueries. The first twosubqueries are identified as being associated with a first external datasystem 12. The other two subqueries are identified as being associatedwith second and third external data systems 12, respectively.Furthermore, the subqueries associated with the third and fourth entriesare in a query language different from each other and different from thesubqueries associated with the first external data system 12.

In the illustrated embodiment, in addition to the query, each entry inthe query portion 6104 includes an identifier for the name of theexternal data system 12 that is to execute the query, the type of query,an estimated number of results to be received from the external datasystem and the number of fields in the results. However, fewer or moreinformation can be included with each subquery as desired.

The deployment portion 6102 can include information related to one ormore external data systems 12 associated with the primary data intakeand query system 16A (non-limiting examples: external data systems 12that can be accessed or searched from the primary data intake and querysystem 16A). The deployment portion 6102 can include locationinformation, access/authorization information, and/or otherconfiguration information about each external system 12 to enable theprimary data intake and query system 16A to interact with and obtaininformation from the external data system 12.

In the illustrated embodiment, the deployment portion 6102 identifiesthree distinct deployments associated with the primary data intake andquery system 16A. One deployment is a Splunk data intake and querysystem and two deployments are third-party data storage and processingsystems (Oracle, Elk). In addition, the deployment portion 6102 includesan IP address, port number, account information, password, type, andversion for each external data system 12. However, it will be understoodthat fewer or more information can be included as desired. As mentioned,in some embodiments, each reference to an external data system 12 in thedeployment portion 6102 can be stored as one or more entries of adatabase.

As a non-limiting example, consider a query received by the data intakeand query system 108 that includes, among others, the following queryparameter: “federated:my_dep_1_search_2.” Based on the identified queryparameter, the query coordinator 3304 can reference the query portion6104 of the external query configuration file 6100 to determine thequery to be executed on an external data system 12. Based on therelevant entry, the query coordinator 3304 can determine that the query“SELECT COUNT (DISTINCT FlightNum) FROM airlinesdata,” is a “streaming”query, is to be executed on the “remote_deployment_2” deployment, shouldreturn fewer than 1,000,000 results and the received results or eventsshould include two different fields.

With continued reference to the example, the query coordinator 3304 canuse the identifier “remote_deployment_2” in the query portion 6104 toadditional information about the corresponding external data system 12.Specifically, the query coordinator 3304 can determine that the“remote_deployment_2” external data system 12 uses port 8089 and can beaccessed using the “ezra_eastwood” account and password “changed” at IPaddress “10.224.126.105.” Moreover, the query coordinator 3304 candetermine that the “remote_deployment_2” external data system 12 is aversion 7 Oracle database.

With continued reference to the example, the query coordinator 3304 can,using the determined information, provide the identified query to theOracle database for processing. As described herein, in certainembodiments, the query coordinator 3304 can translate the identifiedquery from SQL to a different query language, process and/or optimizethe translated query, translate the processed query back to SQL, andcommunicate the translated SQL to the Oracle database for execution. Asmentioned, in some cases, the translated SQL query can be a modified oroptimized version of the SQL query in the external query configurationfile 6100 based on the other portions of the query with which the SQLquery is to be executed.

Although illustrated as two different portions of a single file, thedeployment portion 6102 and the query portion 6104 of the external queryconfiguration file 6100 may be separate files or stored in alternativeformats. Further, the external query configuration file 6100 is notlimited to the illustrated format, but may include any type of datastructure that can be used to store subqueries and/or access informationfor accessing the external data systems 12. For example, each entry ofthe deployment portion 6102 and/or query portion 6104 can be implementedas a database entry of a relational database.

35.0. Bucket Data Distribution for Processing/Export

As described herein, at least with reference to FIGS. 18-20, 26, 27, 33,37, 46, 48, and 51 , one or more indexers 206 can export records fromone or more buckets (also referred to herein as bucket data) to one ormore worker nodes 3306. In some embodiments, each record can correspondto an event or a group of events stored in a data store 208. In somecases, only a portion of different events (e.g., certain field values,keywords, etc.) or data associated with an event (e.g., data about anevent stored in an inverted index) may be obtained/processed as part ofa set of data identified by a query. Accordingly, during queryexecution, an event itself may not be obtained/processed. Instead thedata extracted from one or more buckets may correspond to a portion ofone or more events, a summary of the one or more events, or dataassociated with the one or more events. Accordingly, in some cases,reference may be made herein to processing records, which can correspondto an event or a group of events stored in a data store 208 and/or maycorrespond to an event that has been transformed or processed by acomponent of the system 16.

When a particular indexer 206 processes data or exports bucket data fromone or more buckets to one or more worker nodes 3306, the indexer 206can assign the bucket data corresponding to different buckets to one ormore execution resources, such as one or more pipelines (which may alsobe referred to as data pipelines or data processing pipelines), orcompute resources (e.g., processors, isolated execution environments,etc.).

In some cases, a pipeline can include one or more processing tasks to beexecuted on the bucket data. Further, each pipeline can be concurrentlyexecuted by one or more compute resources of an indexer, such as one ormore processors, isolated execution environments, etc., in order toprocess/export the bucket data in parallel. Accordingly, althoughreference may be made herein to assigning bucket data to a pipeline, itwill be understood that the bucket data is also assigned to one or morecompute resources to process the data assigned to the pipeline. Inaddition, in some cases, multiple compute resources may be assigned toexecute the pipeline (at the same or different times). Further, onecompute resource can concurrently execute multiple pipelines.

In some cases, the assignment of bucket data to execution resources(e.g., bucket data-pipeline assignment) can be skewed such that oneexecution resource processes and/or exports a significantly largeramount of records compared to other execution resources. As anon-limiting example, consider a scenario in which 1) bucket data fromthree buckets is to be sequentially assigned to three executionresources for processing or export, 2) all bucket data from a singlebucket is assigned to the same execution resource, and 3) first bucketdata from the first bucket includes 1,000,000 records, second bucketdata from the second bucket includes 400,000 records, and third bucketdata from the third bucket includes 350,000 records. In such a scenariothe first execution resource, such as a first pipeline, would process1,350,000 records and the second execution resource would process400,000 records. In such a scenario, the system 3301 may lose thepotential benefits provided by concurrently using multiple executionresources to process/export the bucket data.

Moreover, in some cases, the data intake and query system 3301 may beunable to continue executing the query until the last execution resourcefinishes exporting all of its assigned bucket data. In some suchscenarios, other compute resources assigned to execute the pipelines ofthe query may remain idle (not assigned to other tasks) until the lastpipeline completes its data export. In certain embodiments, this candecrease compute resource utilization of the system 3301 (e.g., wastecompute resources), reduce the throughput of the data intake and querysystem 3301, increase the amount of time required by the indexers 206 toprocess and/or export data to the worker nodes 3306, increase the amountof time required by the system 3301 to execute the query, and impair thesystem 3301 from being able to process/execute additional queries.

To address possible issues caused by the potential unequal distributionof bucket data between execution resources of an indexer 206 forprocessing and/or export to one or more worker nodes 3306, the system3301 can assign the bucket data from the buckets associated with a queryto execution resources (e.g., pipelines, processors, etc.) based on abucket distribution policy. In certain embodiments, the bucketdistribution policy can be based on the content of the bucket data, suchas the amount of bucket data to be exported from each bucket. In somecases, the amount of bucket data can correspond to the number of eventsor records to be exported. In some embodiments, the amount of data cancorrespond to the amount of memory used to store the bucket data (e.g.,number of bytes, etc.).

In certain embodiments, the bucket distribution policy can indicate thatthe indexer 206 is to assign the bucket data to different pipelines toreduce or minimize the difference in the amount of bucket data processedby each pipeline. As a non-limiting example, if the indexer 206 isassigning bucket data that includes 30,000,000 records between fivepipelines, the indexer 206 can assign the bucket data so that eachpipeline processes approximately 6,000,000 records. In some cases, itmay not be possible for the indexer 206 to obtain a complete equitabledistribution between pipelines. For example, with continued reference tothe above example, the 30,000,000 records may be unequally distributedacross 13 buckets, and the indexer 206 may distribute the recordsbetween the pipelines by bucket (e.g., assign all bucket data from aparticular bucket to the same pipeline). Accordingly, in some suchcases, the indexer 206 can, according to the bucket distribution policy,assign the bucket data corresponding to the 13 buckets to the fivepipelines so as to reduce or minimize the difference between the numberof records assigned to each pipeline. For example, the indexer 206 mayassign the buckets (e.g., the bucket data corresponding to the buckets)to the five pipelines as shown in the table below. Accordingly, althougheach pipeline may not process the exact same number of records, theindexer 206 can assign the bucket data to obtain a more equitabledistribution across the pipelines.

No. of Records in No. of Buckets Bucket Data of Pipeline AssignedAssigned Buckets 1 3 5.75M  2 2 6.5M 3 1 5.5M 4 4 5.85M  5 2 6.4M

Assigning the bucket data to pipelines based on the amount of bucketdata of each bucket to be exported can improve the functioning of thesystem 16 itself. For example, by assigning the bucket data to pipelinesbased on the amount of bucket data of each bucket to be exported, thesystem 3301 can increase parallelization of the data processing, as wellas increase compute resource utilization, increase the throughput of thesystem 3301, decrease the amount of time required by the indexers 206 toprocess and/or export data to the worker nodes 3306, decrease queryexecution time, and enable the system 3301 to process/execute additionalqueries in less time.

FIGS. 62A and 62B are block diagrams illustrating an embodiment of theidentification of bucket data associated with a query, the allocation ofexecution resources of an indexer 206 to process/export the bucket data,and an assignment of the bucket data to the execution resources based ona bucket distribution policy. Although described with respect toassigning bucket data between execution resources of one indexer 206, itwill be understood that multiple indexers 206 of the system 3301 canconcurrently assign respective buckets to respective execution resourcesin a similar manner.

In the illustrated embodiment of FIG. 62A, the indexer 206 hasidentified bucket data 6204A, 6204B, 6204C, 6204D, 6204E, 6204F(individually or collectively referred to as bucket data 6204) from sixbuckets for processing and/or exporting to one or more worker nodes 3306and has allocated three execution resources 6202A, 6202B, 6202C(individually or collectively referred to as execution resources 6202)based on an execution resource allocation policy to process and/orexport the bucket data 6204. As described herein, when the executionresources are implemented as a pipeline, each processor of an indexer206 can concurrently process one or more pipelines. Accordingly, it willbe understood that the three execution resources 6202A, 6202B, 6202C maybe executed by three processors of an indexer 206 and/or one processorof an indexer 206.

As described herein, in some embodiments, the system 3301 can identifythe bucket data 6204 based on a received query. In some embodiments, thequery can identify a set of data and a manner of processing the set ofdata. For example, as described herein, the query can identify the setof data based on one or more query parameters, such as, but not limitedto, a particular index (or data store partition), a time range, one ormore field-value pairs, and/or one or more keyword or token values, etc.As the system 3301 processes the query, it can determine one or moresubqueries. For example, the system 3301 can determine that a firstportion of the query is to be executed by the one or more indexers 206and a second portion of the query is to be executed by one or moreworker nodes 3306. Based on the different portions of the query, thesystem 3301 can generate a first subquery for the indexers 206 and asecond subquery for the worker nodes 3306. The first subquery canidentify at least a subset of the set of data that can be obtained, andin some cases processed, by the indexers 206. Each indexer 206 can usethe first subquery to identify the relevant buckets that may include atleast a portion of the set of data. In certain embodiments, an indexer206 uses one or more query parameters to identify the relevant buckets.For example, the indexer 206 can use an identified index (or partition)and/or a time range to identify relevant buckets.

The indexer 206 can use one or more query parameters to identify bucketdata that forms at least a portion of the set of data (or is associatedwith the query). For example, the indexer 206 can use an identifiedindex (or data store partition), time range, one or more field-valuepairs, and/or one or more keyword or token values, etc., to identifyrelevant bucket data. As described herein, in some embodiments, thebucket data can include one or more events or records.

In some cases, identifying the bucket data includes identifying eventsand a number of events from each bucket that are associated with thequery. In some embodiments, the indexer 206 can identify the number ofevents of each bucket based on the one or more query parameters. Incertain cases, the indexer 206 can use an inverted index, similar to theinverted indexes described herein at least with reference to FIG. 5B,associated with a particular bucket to identify the events and thenumber of events that satisfy the query parameters. For example, theindexer 206 can compare the time range of the query with the timestampof the events, or the fields, field-value pairs, or keywords of thequery with the corresponding information of the events to identify theevents (and the number of events) that satisfy the query parameters. Incertain embodiments, the indexer 206 can identify the events and thenumber of events based on a comparison of one or more query parameterswith the event data itself (e.g., without an inverted index).

In the illustrated embodiment of FIG. 62A, the indexer 206 hasidentified bucket data 6204A, 6204B, 6204C, 6204D, 6204E, 6204F from sixbuckets that form at least a subset of data of the set of dataidentified by the query. In addition, the indexer 206 has determined thenumber of events or records in each bucket that is associated with thequery. Specifically, in the illustrated embodiment, the indexer 206 hasdetermined that the bucket data includes following number of records, asfollows:

Bucket Data No. of Records 6204A 65M 6204B 110M  6204C 20M 6204D 30M6204E 70M 6204F 50M

As mentioned, in addition to identifying the bucket data 6204, theindexer 206 has allocated execution resources 6202A, 6202B, 6202C toprocess the bucket data 6204 based on an execution resource allocationpolicy. In some embodiments, the execution resource allocation policycan use a variety of factors to determine the number of executionresources to be allocated by the indexer 206.

In some cases, the execution resource allocation policy can be based onthe number of buckets to export (e.g., allocate three executionresources for bucket data from three buckets), the amount of bucket data(e.g., allocate more execution resources for larger quantities ofrecords/events, memory size of bucket data, etc.), the destination ofthe bucket data (e.g., allocate more execution resources for export toworker nodes 3306), the number of worker nodes 3306 assigned to ingestthe bucket data (e.g., allocate one or more execution resources perassigned worker node 3306), the number of processors/pipelines allocatedto ingest the bucket data (e.g., allocate one or more pipelines for eachprocessor of a worker node 3306 that is to ingest the bucket data),amount of execution resources available (e.g., allocate as manypipelines that can be supported by the available processors based on theamount of pipelines allocated to other processing tasks), based on athreshold number (e.g., allocate five or twelve execution resources foreach export), an identification of a user or service level (allocatemore execution resources for one user/service level than anotheruser/service level), and/or a combination thereof. In some embodiments,the execution resource allocation policy can indicate that executionresources are to be allocated to minimize the number of executionresources allocated, minimize the execution time of the bucket data,provide a particular priority level (e.g., different levels of serviceare assigned a different number of execution resources), maximizeparallelization up to a threshold number of execution resources or anyone or any combination of the aforementioned. In some cases, theexecution resource allocation policy can take into account the bucketdistribution policy, etc. For example, the indexer 206 can allocate acertain number of execution resources, then assign the buckets to theexecution resources based on a bucket distribution policy in such a waythat one or more execution resources are deallocated or added to processthe data.

As mentioned, the execution resource allocation policy can use multiplefactors to allocate execution resources. In certain embodiments, theexecution resource allocation policy can indicate that the number ofexecution resources allocated to process the buckets is based on thelesser of: the number of buckets associated with the query, the numberof available execution resources (e.g., available pipelines orprocessors), and a threshold number of execution resources. For example,if there is bucket data from five buckets to be processed, sevenavailable execution resources, and the threshold number of executionresources is six, the system can allocate five execution resources toprocess the bucket data. With reference to the same example but withbucket data from nine buckets to be processed, the indexer 206 canallocate six buckets (the threshold number). In the event the thresholdnumber were eight, the indexer 206 could allocate seven executionresources (the number of available resources). Accordingly, theexecution resource allocation policy can take into account a variety offactors for determining the number of execution resources to allocate toprocess the bucket data.

As another example, the execution resource allocation policy can be toreduce or minimize the total execution time or maximize parallelization.For example, if an indexer 206 identifies bucket data from three bucketsfor processing, the indexer 206 can allocate three execution resourcesto process the three buckets. In certain embodiments, the indexer 206can allocate the three execution resources regardless of the amount ofbucket data of each bucket to be processed. For example, if first bucketdata includes 1,000,000 records, second bucket data includes 500,000records, and third bucket data includes 550,000 records, the indexer 206can allocate three execution resources even though two executionresources will be underutilized while one execution resource finishesprocessing the first bucket data.

Returning to FIG. 62A, the indexer 206 has allocated three executionresources 6202A, 6202B, 6202C based on an execution resource allocationpolicy to process and/or export bucket data 6204 from six buckets.

In the illustrated embodiment of FIG. 62B, the indexer 206 has assignedthe bucket data 6204 to the execution resources 6202 based on a bucketdistribution policy. The bucket distribution policy can use a variety offactors to determine how the bucket data 6204 is to be assigned toexecution resources 6202.

In some embodiments, the bucket distribution policy can be based on thetime of receipt of the bucket data. For example, the indexer 206 canassign the bucket data to the execution resources 6202 as it is received(e.g., first bucket data 6202A assigned to first execution resource6204A, second bucket data 6204B assigned to second execution resource6202B, and so on in a round robin fashion). In certain embodiments, thebucket distribution policy can indicate that the bucket data 6204 is tobe assigned in a randomized fashion. In some embodiments, the bucketdistribution policy can indicate that the bucket data 6204 is to beassigned to execution resources 6202 based on the content of the bucketdata. In some such cases, the bucket distribution policy can indicatethat bucket data 6204 is to be assigned to execution resources 6202 toreduce or minimize the differences between the amount of bucket dataprocessed by the different execution resources 6202. For example, thebucket distribution policy can indicate that bucket data 6204 is to beassigned to execution resources 6202 such that each execution resource6202 processes approximately the same number of events, records, orbytes of data.

In certain embodiments, the bucket distribution policy can indicate thatbucket data 6204 is to be assigned to execution resources 6202 to reduceor minimize the difference in execution time between execution resources6202. For example, a unit of bucket data from one bucket (e.g., arecord) may generally take longer to process than a unit of bucket datafrom another bucket. Accordingly, the indexer 206 can use a (estimated)execution time for each unit of bucket data or the bucket data as awhole to allocate execution resources. For example, if 5 million eventsfrom one bucket and 2 million are each estimated to take 1 minute toprocess, then the indexer 206 can use the estimated execution time toassign the buckets to execution resources instead of (or in conjunctionwith) the number of events from the different buckets. In certainembodiments, the system 16 can determine the estimated execution time byapplying one or more processing tasks (e.g., transformation, exports,etc.) to a subset of events of a bucket and using the result to estimateexecution time of other events of the bucket or other events within thetime range of the subset of events or with a similar sourcetype or otherfield-value of the subset of events. The estimation can be performedduring the query or beforehand by the system. For example, the system 16can, on occasion, sample bucket data from different buckets to determinethe estimate execution time. The estimate can be stored in aconfiguration file for later use during the query. In certain cases, theestimated execution time can be determined in a manner similar to theevent generation estimate described herein at least with reference toFIG. 67 (e.g., processing a sample set of data to determine theestimated execution time and storing various estimated execution timesin a lookup table or configuration file).

It will be understood that any one or any combination of theaforementioned methodologies can be used by a bucket distribution policyof an indexer 206 to determine how to assign bucket data to executionresources. Furthermore, different indexers 206 can use different bucketdistribution policies as desired.

In the illustrated embodiment of FIG. 62B, the indexer 206 has assignedthe buckets 6204 to the execution resources 6202 based on a bucketdistribution policy that prioritizes an equal distribution of records ofthe bucket data 6204 to execution resources 6202. Based on the bucketdistribution policy, the indexer 206 assigns the bucket data 6204 to theexecution resources 6202 as follows:

Execution Resource Bucket Data Total Records 6202A 6204A 6204F 115M6202B 6204B 110M 6202C 6204C 6204D 120M 6204E

While the number of buckets assigned to each execution resource variesas much as three times, the largest difference between the amount ofrecords processed by two execution resources (execution resource 6202Aand 6202C) is ten million with the mean number of records assigned toeach execution resource 6202 being 115 million. By comparison, if theindexer 206 sequentially assigned the bucket data 6204 in a round robinfashion or based on a priority of equal distribution of buckets toexecution resources 6202, the bucket distribution may have been asfollows:

Execution Resource Bucket Data Total Records 6202A 6204A 6204D 95M 6202B6204B 180M  6204E 6202C 6204C 70M 6204F

While the mean number of records assigned to each execution resource6202 remains 115 million, the largest number of records assigned to anexecution resource (execution resource 6202B) is 180 million and thelowest 70 million (execution resource 6202C). As such, executionresource 6202B would process and/or export more than twice as manyrecords as execution resource 6202C, which could take more than twice aslong. Thus, by using a bucket distribution policy that prioritizes theequal distribution of records of the bucket data 6204 to executionresources 6202, the indexer 206 can reduce the amount of execution timeto process and/or export the bucket data to the worker nodes 3306.

In some embodiments, to determine the bucket assignment, the indexer 206can determine an average or mean number of records for each executionresource based on the total number of records to be processed and thenumber of execution resources 6202 allocated. The indexer 206 can thenassign bucket data 6204 to the execution resources 6202 so that eachexecution resources 6202 is assigned a number of records that mostclosely approximates the average or mean. In certain embodiments, theindexer 206 can compare various combinations of bucket-executionresource assignments until an assignment is provided that results ineach execution resource 6202 being assigned approximately the samenumber of records or results in the smallest difference between theexecution resource 6202 with the most records and the execution resource6202 with the fewest records. It will be understood that a variety ofequal distribution models can be used to distribute the bucket data in amanner that increases the likelihood that each execution resource 6202processes approximately the same amount of bucket data.

Furthermore, it will be understood that the bucket distribution policyand the execution resource allocation policy can be used iteratively todetermine the number of execution resources 6202 allocated and theassignment of bucket data 6204 to execution resources 6202. In somecases, the indexer 206 can iteratively use the bucket distributionpolicy and the execution resource allocation policy to allocateexecution resources 6204 and assign bucket data to execution resources6202.

For example, consider a scenario where the execution resource allocationpolicy is to prioritize the parallelization of and efficient useexecution resources 6202. In such a scenario, the indexer 206 mayinitially allocate three execution resources 6202 to process bucket datafrom the following three buckets:

No. of Records Bucket Data in Bucket Data A   3M B 1.45M C  1.5M

While such an allocation would result in a highly parallelizeddistribution, the distribution could result in compute resourcesassigned to process bucket data B and C being underutilized while acompute resource assigned to process the execution resource 6202(assigned to bucket data A) processes more than twice as many records.

In such a scenario, if the bucket distribution policy is to reduce orminimize differences between the amount of bucket data 6204 (e.g.,number of records) assigned to execution resources 6202, the indexer 206may determine that an allocation of two execution resources 6202significantly reduces the difference between the largest and smallestnumber of records assigned to different execution resources 6202.Accordingly, the indexer 206 may deallocate one execution resource 6202and assign the buckets as follows:

Execution Bucket Data No. of Resource Assigned Records Assigned 1 A   3M2 B, C 2.95M

Accordingly, the indexer 206 can iteratively use the execution resourceallocation policy and bucket distribution policy to determine the numberof execution resources to allocate and how to assign bucket data 6204 toexecution resources 6204. In some such cases, the indexer 206 can, afterdetermining an initial execution resource allocation, determine whetheran allocation of one or more additional or fewer execution resourceswould result in a more equitable distribution of bucket data toexecution resources. If it does, the indexer 206 can deallocate one ormore execution resources or allocate one or more additional executionresources. In some such embodiments, in determining whether todeallocate execution resources, the indexer 206 may determine whether adeallocation of resources increases execution time by a threshold amount(e.g., increases estimated execution time by 10%). If it does then theindexer 206 may not deallocate one or more execution resources eventhough doing so may result in a more equitable distribution of bucketdata. Similarly, in determining whether to allocate additional executionresources, the indexer 206 can determine whether doing would decreaseexecution time by a threshold amount. If not, then the indexer 206 maynot allocate additional execution resources.

As yet another example, in some embodiments, the indexer 206 candetermine one or more bucket distributions, and use the bucketdistributions to determine the execution resource allocation. Forexample, consider a scenario in which 13 buckets are to beprocessed/exported. The indexer 206 can determine that the buckets aremost evenly distributed if four execution resources are allocated, thatthe buckets would be processed the fastest with thirteen executionresources, and/or that the addition of execution resources above sevendoes not significantly decrease the execution time (non-limitingexample, does not decrease execution time by more than 10%). Theexecution resource allocation policy can use this information todetermine the number of execution resources to allocate. As mentioned,other combinations of factors can be used to determine the number ofexecution resources to allocate and how to assign buckets to theexecution resources.

FIG. 63 is a flow diagram illustrative of an embodiment of a routine6300 implemented by an indexer 206 to assign bucket data 6202 toexecution resources 6204. Although certain blocks are described as beingimplemented by an indexer 206, it will be understood that the elementsoutlined for routine 6300 can be implemented by one or more computingdevices/components (alone or in combination) that are associated with adata intake and query system 16, such as the search head 210, searchprocess master 3302, the query coordinator 3304, etc. Thus, thefollowing illustrative embodiment should not be construed as limiting.Moreover, it will be understood that routine 6300 is not limited to adata intake and query system 16, but can be used to allocate groups ofdata to execution resources in a variety of systems and environments.

At block 6302, the indexer 206 receives a query. In some embodiments,the received query can correspond to a portion of a query received bythe search head 210 or query coordinator 3304 or a subquery generated bythe search head 210 and/or query coordinator 3304 as described herein atleast with reference to FIGS. 6, 20, 33, 37, 38, and 41 . As describedherein, the query received by the indexer 206 can identify a set of dataand a manner of processing the set of data. In some embodiments, the setof data identified in the query received by the indexer 206 cancorrespond to a subset of data identified by the query received by thesearch head 210 and/or query coordinator 3304. Further, as describedherein, the query can indicate that the indexer 206 is to export the setof data to one or more nodes 3306 for further processing.

At block 6304, the indexer 206 identifies buckets associated with thequery. In some embodiments, the indexer 206 identifies the bucketsassociated with the query based on one or more query parameters, suchas, but not limited to an identified index or partition, a time range,etc. For example, in some cases, the indexer 206 can determine that abucket is associated with the query if the bucket is associated with anindex identified in the query and/or at least partially overlaps with atime range specified in the query. In certain embodiments, the indexer206 can use fewer or more criteria to identify buckets associated withthe query.

At block 6306, the indexer 206 identifies bucket data associated withthe query. In some embodiments, the indexer 206 identifies bucket dataassociated with the query for one or more (or all) of the bucketsidentified at block 6304. For example, if the indexer 206 identifies tenbuckets at block 6306, the indexer 206 can identify bucket data from theten buckets that are associated with the query.

In certain embodiments, to identify the bucket data associated with thequery for a particular bucket, the indexer 206 uses one or more queryparameters from the query. For example, the indexer 206 can use one ormore of the index(es), a time range, fields, field-value pairs, keywordor tokens, etc., identified in the query to identify bucket dataassociated with the query.

In some cases, identifying the bucket data includes identifying a numberof events associated with the query. In certain embodiments, to identifythe number of events (and/or the events) associated with the query, theindexer 206 can use the one or more query parameters of the query and aninverted index, similar to inverted indexes 507, 509, described herein.In some cases, the indexer 206 can use the inverted indexes to determinethe number of events that satisfy the query parameters. In some suchembodiments, the indexer 206 can determine that the events that satisfythe query parameters form part of the set of data of the query.

At block 6308, the indexer 206 determines execution resources toallocate for the query. As described herein, in some cases, the indexer206 can determine the execution resource to allocate based on anexecution resource allocation policy. As described herein, the executionresource allocation policy can indicate whether to prioritizeparallelization, efficient use of execution resources, minimize numberof execution resources, etc. In some embodiments, the execution resourceallocation policy can indicate that the indexer 206 is to allocateexecution resources based on the lesser of the number of identifiedbuckets, the number of available execution resources or a thresholdnumber of execution resources. In certain embodiments, the executionresource allocation policy can indicate that the indexer 206 is toallocate execution resources based on the total number of identifiedevents. For example, the execution resource allocation policy canindicate that the average or mean number of records assigned to anexecution resource should not exceed a threshold number of records andallocate sufficient execution resources to satisfy the threshold, orthat a maximum threshold number of records is to be assigned to anyparticular execution resource and allocate the execution resources tosatisfy the maximum threshold, etc. In certain embodiments, theexecution resource allocation policy can indicate that the indexer 206is to allocate execution resources based on a particular priority levelto be provided for the query. Accordingly, the indexer 206 can allocateresources based on the quantity of records, content of the buckets, orbucket data, etc.

At block 6310, the indexer 206 assigns buckets (e.g., bucket data ofbuckets) to execution resources. In some embodiments, the indexer 206can assign the buckets to the execution resources based on the contentof the bucket/bucket data and/or a bucket distribution policy. Asdescribed herein, the bucket distribution policy can indicate to theindexer 206 whether to prioritize an equitable distribution of bucketsto execution resources, an equitable distribution of records or amountof bucket data or memory space of bucket data to the executionresources, etc. For example, the indexer 206 can assign buckets toexecution resources so as to reduce or minimize a difference between themost and least records assigned to particular execution resources and/orto approximate a particular number of records assigned to each executionresource. Moreover, as described herein, the indexer 206 can iterativelyuse an execution resource allocation policy and a bucket distributionpolicy to allocate execution resources for the query and assign bucketsto the execution resources.

At block 6312, the indexer 206 processes the bucket data. In someembodiments, processing the bucket data can include performing one ormore transformations on the bucket data and/or exporting the bucket datato one or more worker nodes 3306, based on the query.

In embodiments where the bucket data is exported to one or more workernodes 3306, the indexer 206 can communicate the bucket data to theworker nodes 3306 as one or more chunks of data. In some embodiments,each chunk of data can include a particular number of records. Incertain embodiments, each chunk of data (except, in some cases, the lastchunk of data) can include the same number of records. In someembodiments, each chunk of data can occupy a particular amount of memoryspace. In certain embodiments, each chunk of data (except, in somecases, the last chunk of data) can occupy (approximately) the sameamount of memory space. Further, the worker nodes 3306 can process thechunks of data based on a query they received from a search head 210and/or query coordinator 3304. In addition, it will be understood thatthe worker nodes 3306 can concurrently receive chunks of data frommultiple indexers 206.

It will be understood that fewer, more, or different blocks can be usedas part of the routine 6300. For example, the routine 6300 may omitblocks 6302 and 6312. As another example, in some embodiments, aplurality of indexers 206 can concurrently perform the routine 6300.Further, in certain embodiments, the system 16 can estimate a queryexecution time based on the bucket distribution. For example, the system16 can identify the slowest execution resource across all of theindexers 206 used as part of the query. Based on the identified slowestexecution resource, the system can estimate the execution time of theportion of the query received by the system that is to be executed bythe indexers 206.

In some embodiments, to identify the slowest execution resource, thesystem 16 can identify the execution resource that has the most bucketdata (based on number of records, memory size) to process and/or isestimated to take the most time based to process the assigned bucketdata (e.g., based on a speed of the execution resource, the number ofrecords assigned to the execution resource, the sourcetype of therecords assigned to the execution resource, and/or a measured speed ofthe compute resource assigned to execution resource for the particularrecords assigned thereto, etc.). In some cases, the system 16 candetermine an estimated execution time by applying a processing task to aset of data and determining the time to process the set of dataaccording to the processing task. The estimate can be made before orduring the query processing/execution and then be applied to data thatis similar to the set of data (e.g., same index, source type and/or timerange, etc.). In embodiments where the estimates are made before queryprocessing/execution, they can be stored in a configuration file forlater use.

Moreover, it will be understood that one or more blocks described hereinwith reference to routine 6300 can be combined with one or more blocksof other routines described herein, such as the routines describedherein at least with reference to FIGS. 5, 6, 23-26, 31, 34, 38-45, 47,49, 52-57, 65-69, 71, and 73 . Furthermore, it will be understood thatthe various blocks described herein with reference to FIG. 63 can beimplemented in a variety of orders. For example, blocks 6304-6308 can beimplemented concurrently, etc.

36.0. Partitioning and Reducing Records During Ingest at a Worker Node

As described herein, at least with reference to FIGS. 18-20, 26, 27, 33,37, 46, 48, and 51 , one or more worker nodes 3306 can receive chunks ofdata from one or more indexers 206. When a particular worker node 3306receives and/or processes a chunk of data, the worker node 3306 canassign the chunk of data to different tasks or partitions for executionby one or more compute resources, such as one or more processors orisolated execution environments, etc. Although reference may be madeherein to storing data in partitions of the worker nodes 3306, it willbe understood that such storing can refer to storing data in volatilememory and/or non-volatile memory with spill over to disk ifneeded/desired.

In some cases, prior to performing one or more transformations on thereceived data, the worker node 3306 groups and/or reduces the data tofacilitate more efficient processing of the data. For example, in theevent that the data received by the worker node 3306 is unstructured,the worker node 3306 can reorganize the unstructured data (e.g., assignsimilar data to the same partition, reduce similar data, etc.) tofacilitate a more efficient processing of the data. Further, in somesuch embodiments, the worker node 3306 receives all of the data to beprocessed and assigns it to different partitions before grouping andreducing the data. For example, for unstructured data, the worker node3306 may not attempt to group the data until it has all been received.In either case, both redistributing and reducing the data can increasethe execution time of the query; more so in cases where the worker node3306 waits to receive all of the data before attempting to group and/orreduce it.

To address possible issues caused by grouping and/or reducing data atingest and/or waiting to group/reduce until all of the data has beenreceived and assigned to partitions, a worker node 3306 can logicallyassign incoming data based on its content to different groups of data.Data assigned to distinct groups can be assigned to the same partitionor the same group of partitions for further processing. By logicallyassigning incoming data to different groups based on its content, theworker node 3306 can increase the likelihood that similar records areassigned to the same partition. In addition, by logically assigningincoming data to different groups based on its content on ingest, theworker node 3306 can reduce the number of processing tasks used toredistribute and reduce the data in the different partitions, which canreduce the query execution time.

In addition, to address possible issues caused by redistributing and/orreducing data at ingest and/or waiting to redistribute/reduce until allof the data has been received and assigned to partitions, the workernode 3306 can combine similar data as the data is assigned to particularpartitions at ingest. While the logical assignment of records based oncontent can increase the likelihood that records with similar data areassigned to the same group (and thus the same partition), it will beunderstood that combining similar records during ingest canindependently improve the functioning of the system 16. For example, bycombining similar records in a partition during ingest, the worker node3306 can reduce the amount of memory used to store all of the incomingrecords and possibly reduce the number of partitions used to store theincoming data. In addition, by combining similar records in a partitionduring ingest, the worker node 3306 can reduce the number of processingtasks used to reduce the data in the different partitions and reducequery execution time, thereby increasing throughput of the system 16.For example, the worker node 3306 may be able to complete the data groupand reduction in one processing task or fewer processing tasks than itwould if it did not combine data during ingest and/or assign records topartitions based on the content of the records.

FIG. 64 is a block diagram illustrating an embodiment of a worker node3306 ingesting four chunks of data (chunks 7, 8, 9, 10), assigning therecords of the chunks of data to various partitions (partitions 1A, 1B,1C, 2A, 2B, 2C, 3A, 3B), and reducing the records of the partitions.

As described herein, each indexer 206 can form chunks of data frombucket data associated with one or more buckets. In some cases, eachchunk of data can include a similar or the same number of records. Forexample, each chunk of data can be assigned approximately 50,000 recordsor records or some other threshold number of records. Accordingly, insome embodiments, the chunks 7-10 can correspond to bucket data receivedfrom the indexers 206 and each record of a chunk of data can correspondto a record of the bucket data.

In certain embodiments, chunks 7-10 may each correspond to a portion ofa chunk of data received from one or more indexers 206. For example, aschunks of data are received by a worker node 3306, the worker node 3306may break up the chunks of data into sub-chunks of data and place thesub-chunks of data in a data buffer for assignment to one or morepartitions. Accordingly, chunks 7-10 may correspond to a chunk of datareceived by the worker node 3306 or a sub-chunk of data generated by theworker node 3306. Further, it will be understood that one or more chunksof data may precede chunks 7-10 and/or may follow chunks 7-10.

In the illustrated embodiment of FIG. 64 , the four chunks of data(chunks 7, 8, 9, 10) each contain a plurality of records where eachrecord includes a keyword value and a count. However, it will beunderstood that each record can include one or more key values, one ormore field values, one or more counts, or other information, and/or canbe based on the content of events/records received from the indexers206. Thus, the example records illustrated in FIG. 64 should not beconstrued as limiting.

In the illustrated embodiment, chunk 7 includes eight records with thefollowing key value and count (A:2, B:3, C:5, D:7, E:4, F:3, G:8, H:9),chunk 8 includes six records with the following key value and count(C:4, D:6, E:7, F:5, H:2, 1:8), chunk 9 includes nine records with thefollowing key values and counts (A:7, B:5, C:4, D8, E:3, F:6, G:9, H:1,1:2) and chunk 10 includes four records with the following key valuesand counts (B:4, D:3, G:2, 1:5). Although the records of each chunk areshown in alphabetical order, it will be understood that the records maybe sorted in any manner or not sorted.

In the illustrated embodiment, the records are assigned to one of threegroups (Group 1, Group 2, Group 3). However, it will be understood thatfewer or more groups can be used as desired. In addition, the workernode 3306 can use a variety of methods to determine the number oflogical groups. In some cases, the number of logical groups cancorrespond to the number of processor cores or compute resources of theworker node 3306 that are allocated to process the data.

In some embodiments, as the worker node 3306 ingests the chunks 7-10 andassigns the records of the chunks 7-10 to partitions, the worker node3306 can logically group the records based on their content. In theillustrated embodiment of FIG. 64 , the worker node 3306 groups therecords of the chunks 7-10 based on the keyword value and assigns eachrecord to one of three record groups (Group 1, Group 2, Group 3) basedon the keyword value of the record. It will be understood that theworker node 3306 can logically assign records to different groups usinga variety of techniques and/or content of the record. For example, theworker node 3306 can assign records based on the content of one or morekeyword values, one or more fields or field values, one or morefield-value pairs, counts, etc., or any combination thereof. In someembodiments, the portion of the record used to assign the record can bebased on the query. For example, the worker node 3306 can use a keywordvalue or field value that is identified as being used in the query toreduce or summarize the records. In some embodiments, the keyword orfield value identified in the query can correspond to a keyword or fieldvalue that corresponds to one or more events stored in a data store 208(e.g., sourcetype, keyword, etc.), a field value generated during theprocessing of the query (e.g., count), and/or a field value that is usedby the worker node 3306 to process and/or transform the records, etc.For example, if the query includes a command “group by field_name,” theworker node 3306 can use the field values of the identified field_nameto logically group the records.

In some embodiments, to assign a record to a particular group, theworker node 3306 uses a hash code, hash value, or identifier of eachrecord and/or applies a hash function or modulo operator to a particularkeyword or field value. For example, with respect to the illustratedembodiment, the worker node 3306 can apply modulo 3 operand to thekeyword value of a record to assign it to Group 1, Group 2, or Group 3.Based on the modulo three operand records with keyword values A, D, G,J, and so on, are assigned to Group 1, records with keyword values B, E,H, K, and so on, are assigned to Group 2, and records with keywordvalues C, F, I, L, and so on, are assigned to Group 3. In certainembodiments, the worker node 3306 uses a lookup table to assign recordsto a particular group.

In some embodiments, the logical assignment of the records to thedifferent groups can be used by the worker node 3306 to assign therecords to a particular partition or group of partitions. For example,based on the assigned group, the worker node 3306 can assign records toa particular partition or a particular group of partitions. In somecases, each group of partitions can be associated with one of thelogical groups. Thus, if the records are assigned between four logicalgroups there can be four groups of partitions to which the record can beassigned.

In some embodiments, the worker node 3306 can predetermine the number ofpartitions to receive the records. In certain embodiments, the workernode 3306 can determine the total number of partitions to receive therecords based on the number of cores of and an amount of memoryallocated by the worker node 3306.

In certain embodiments, the partitions can be pre-assigned to aparticular group of partitions (e.g., at creation or prior to beingneeded to store records) or assigned during processing. For example, thetotal number of partitions can be equally distributed to the differentgroups of partitions or distributed based on an estimated amount of datato be assigned to each group. As another example, as one partition of aparticular group of partitions is filled with records, another partitioncan be assigned to the group of partitions to accept additional recordsthat are assigned to the group.

During ingest, partitions of a group of partitions can be filledsequentially. For example, as one partition of a group of partitions isfilled, another partition of the group can be used to receive additionalrecords. It will be understood that as the number of records assigned toeach group of partitions varies, different groups of partitions can swapout partitions at different times. Further, at any given time the numberof partitions in any particular group may vary from the number ofpartitions in another group of partitions. For example, with referenceto FIG. 64 , after processing Chunk 7, three partitions (partitions 1A,1B, and 1C) are associated with Group 1 and form a first group ofpartitions, three partitions (partitions 2A, 2B, and 2C) are associatedwith Group 2 and form a second group of partitions, and two partitions(partitions 3A and 3B) are associated with Group 3 and form a thirdgroup.

It will be understood that in assigning records to partitions, aparticular partition of a group may be unable to receive every recordassigned to its group from a particular chunk. As such, records from thesame chunk of data that are assigned to the same logical group may beassigned to different partitions of the corresponding group ofpartitions. For example, with reference to FIG. 64 , partition 2Cincludes a record (K:4) that corresponds to a chunk of data thatpreceded Chunk 7.

With continued reference to FIG. 64 , following the processing of Chunk7, partition 1C includes three records with the following keyword valuesand counts (A:2, D:7, G:8), partition 2C includes four records with thefollowing keyword values and counts (K:4, B:3, E:4, H:9), and partition3B includes two records with the following keyword values and counts(C:5, F:3). Other than the K:4 record, the records found in partition1C, 2C, and 3B correspond to records of chunk 7. As mentioned, the K:4record can correspond to a record found in a chunk that preceded Chunk7. For example, partition 2B may have been unable to accept the K:4record due to its capacity having been reached.

Although reference is made to processing Chunk 7 and then processingChunks 8-10, it will be understood that the worker node 3306 can processthe chunks 7-10 serially or in parallel. For example, the worker node3306 can assign the records of chunk 7 to one or more partitions first,then assign the records of chunk 8, and so on, or can use multiplecompute resources to concurrently assign records from chunks 7-10 to oneor more partitions. For example, consider a scenario in which threecompute resources are used to concurrently assign records from chunks7-10 to one or more partitions. In such a scenario, chunks 7-9 can eachbe assigned to a compute resource. The first compute resource thatcompletes its task can then begin processing chunk 10. In some cases,the worker node 3306 can assign chunks of data to compute resources in amanner similar to how an indexer 206 assigns bucket data to computeresources for processing, as described herein at least with reference toFIGS. 62 and 63 .

Accordingly, in some embodiments, the chunks 7-10 can be assigned todifferent compute resources. However in some such embodiments, eachchunk is assigned to a single compute resource (e.g., an entire chunk isassigned to one compute resource). Accordingly, in such embodiments, therecords of the different chunks 7-10 can be assigned to partitions inparallel, but the records within a particular chunk may be assignedserially.

As mentioned, as records are assigned to a particular partition of agroup of partitions, they can be combined with similar records. In somecases, to determine whether records are similar, the worker node 3306can compare the one or more keyword values and/or field values of therecords. If one or more keyword and/or field values match, the recordscan be considered similar and can be combined. In certain embodiments,keyword and/or field values used to identify similar records can be canevent field values or field values that correspond to the field valuesof the event(s) related to the record, as opposed to generated fieldvalues or values generated during query execution. By combining similarrecords from the chunks into a record in a partition, the worker node3306 can reduce the resulting number of records in the partitions and/orthe amount of memory used to store the ingested records. In some cases,this can reduce the number of partitions of a group of partitions andreduce the execution time of the query.

In the illustrated embodiment of FIG. 64 , as the worker node 3306processes the chunks 8-10 it combines similar records. For example, theworker node 3306 combines all ‘A,’ records assigned to partition 1C intoa single record. Similarly, the worker node 3306 combines all ‘D’ and‘G’ assigned to partition 1C into two records; all ‘K,’ ‘B,’ ‘E,’ and‘H,’ records assigned to partitions 2C into four records, and ‘C,’ ‘F,’and ‘I,’ records assigned to partition 3B into three records. Whencombining the records, the worker node 3306 can remove some or all ofthe similar records except one and aggregate at least one field value ofthe removed similar records to the remaining similar record. Forexample, if there are five similar records, the worker node 3306 canremove four records and aggregate a count field value from all fiverecords into the remaining record, thereby reducing the total number ofcounts. In certain embodiments, the worker node 3306 aggregates agenerated field value from the similar records.

With continued reference to FIG. 64 , after chunks 8-10 are processed,partition 1C includes three records with the following keyword valuesand counts (A:9, D:24, G:21), partition 2C includes four records withthe following keyword values and counts (K:4, B:12, E:14, H:12), andpartition 3B includes three records with the following keyword valuesand counts (C:13, F:14, 1:15). As a non-limiting example, by combiningsimilar records, the worker node 3306 combines the records from chunks7-10 as shown:

Keyword No. of Chunk No. of Partition Value Records Records A 2 1 B 3 1C 3 1 D 4 1 E 3 1 F 3 1 G 3 1 H 3 1 I 3 1

Accordingly, by combining similar records, the worker node 3306 reducesthe number of records from chunks 7-10 from 27 records to 9 records.Furthermore, in the illustrated embodiment, by combining similar recordsthe processing of chunks 8-10 added only one new record (compared to thepartitions after processing chunk 7) to any of the partitions: record1:15 in partition 3B. It will be understood that the ratio of thereduction and the number of records added after assigning records fromdifferent chunks will depend on the similarity and timing of the dataassigned to each partition.

In some embodiments, once the records from the chunks of data areassigned to individual partitions of the various groups of partitions,the worker node 3306 can reduce the records across the different groupsof partitions. For example, with reference to FIG. 64 , depending onwhen ‘G’ records (records with a keyword value of ‘G’) are received,there may exist a ‘G’ record in partition 1A and/or 1B, in addition tothe G:21 record in partition 1C. Accordingly, the worker node 3306 cancombine the various ‘G’ records across the partitions 1A-1C to reducethe number of records in the partitions, and potentially reduce thenumber of partitions.

In some cases, as part of reducing records across partitions of aparticular group of partitions, the worker node 3306 can reassignsimilar records from different partitions of the group of partitions tothe same partition. For example, the worker node 3306 can assign all ‘A’records to partition 1A, all ‘D’ records to partition 1B, all ‘G’records to partition 1C, and so on. In some cases, the worker node 3306can reassign similar records in a way that is similar to the logicalassignment of records to different groups of partitions. For example,the worker node 3306 can use a hash or modulo operand to assign therecords between the group of partitions. In some embodiments, the valuefor the modulo operand can correspond to the number of partitions of thegroup of partitions.

In certain embodiments, the worker node 3306 can reassign records of agroup of partitions to the partition that includes a largest count of asimilar record. For example, if partition 1B includes an A:15 record anda D:12 record, the worker node 3306 can assign the A:9 record frompartition 1C to partition 1B and assign the D:12 from partition 1B topartition 1C. It will be understood that a variety of methods can beused to reassign records between a group of partitions.

Once the records have been reassigned (or as they are being reassigned)to the partitions of the group of partitions, the worker node 3306 cancombine the similar records in each partition of the group of partitionsto reduce the total number of records across the group of partitions.With continued reference to the example above, the worker node 3306 cancombine the A:15 record and A:9 record of partition 1B to become onerecord: A:24. Similarly, the worker node 3306 can combine the D:12record and D24 record of partition 1C into one record: D:36. As shown,by combining records, the worker node 3306 removes one or more of thesimilar records and aggregates one field value of the similar recordsinto one remaining record. However, it will be understood that therecords can be combined in a number of ways using a number of keyword orfield values, or other information. In certain cases, the records arecombined based on the query or a processing task of the query.

Following the reduction of similar records across partitions of aparticular group of partitions, the worker node 3306 can continueprocessing the partitions according to the query. As mentioned, bygrouping and reducing records at ingest, the functioning of the workernode 3306 can be improved significantly. For example, grouping andreducing records at ingest can result in fewer records to be processedby the worker node 3306, fewer partitions to store the records, lessmemory being allocated to store the partitions and/or records, fewerprocessing tasks being required to process the data, all of which canlead to improved query execution times and greater throughput by theworker node 3306 and system 16.

FIG. 65 is a flow diagram illustrative of an embodiment of a routine6500 implemented by a worker node 3306 to assign records of chunks ofdata to one or more partitions and combine records of the one or morepartitions. Although certain blocks are described as being implementedby a worker node 3306, it will be understood that the elements outlinedfor routine 6500 can be implemented by one or more computingdevices/components (alone or in combination) that are associated with adata intake and query system 16, such as the search head 210, searchprocess master 3302, the query coordinator 3304, etc. Thus, thefollowing illustrative embodiment should not be construed as limiting.Moreover, it will be understood that routine 6500 is not limited to adata intake and query system 16, but can be used to assign and reducerecords between processors of an execution node in a variety of systemsand environments.

At block 6502, a worker node 3306 obtains a chunk of data. As describedherein, the chunk of data can correspond to a chunk of data receivedfrom an indexer 206 or to a portion of a chunk of data received from anindexer 206. In addition, the chunk of data can include one or moreevents or records. Further, each event or record may have a portion ofraw machine data. In some embodiments, each event or record can includeone or more keyword values, field values, etc.

At block 6504, the worker node 3306 assigns a record to a record group.As described herein, the record can be assigned to one record group of aplurality of record groups. In certain cases, the number of recordgroups of the plurality of record groups can be based on a number ofcompute resources allocated by the worker node 3306 to process incomingchunks of data. For example, if three processors are allocated toprocess incoming chunks of data, the record can be assigned to one ofthree record groups. However, it will be understood that fewer or morerecord groups can be used. For example, the number of record groups maybe greater than or less than the number of compute resources allocatedto process incoming chunks, etc.

In some embodiments, the worker node 3306 assigns the record based oncontent of the record. For example, the worker node 3306 can assign therecord based on any one or any combination of one or more field valuesor one or more keyword values of the record. In certain embodiments, theworker node 3306 assigns records to record groups such that similarrecords (e.g., records with at least one same keyword value or fieldvalue used to assign records to record groups) are assigned to the samerecord group. For example, if the worker node 3306 assigns records basedon an IP address field, the worker node 3306 can assign records suchthat records with the same IP address are assigned to the same recordgroup. In certain embodiments, the worker node 3306 assigns the recordsbased on a modulo operand. In certain cases, the worker node 3306applies the modulus operand to the keyword or field value used to assignrecords to record groups. For example, with reference to the IP addressexample, the worker node 3306 can apply the modulus operand to the IPaddress of a record. The output of the modulus operand can determine towhich record group the record is assigned. In some embodiments, thevalue of the modulo operand can correspond to the number of the recordgroups.

At block 6506, the worker node 3306 assigns the record to a partition ofa group of partitions. As described herein, in some embodiments, eachrecord group can be associated with a group of one or more partitions.Further, each group of partitions can include one or more partitionsthat store or hold one or more records. In some such embodiments, byassigning a record to a record group, the worker node 3306 can alsoassign the record to a group of partitions.

In some cases, the worker node 3306 assigns the record to a partition ofthe group of partitions based on time. For example, as the worker node3306 ingests chunks of data, it can assign the records to a firstpartition of a group of partitions until the first partition is filled.Once filled, the worker node 3306 can assign records to a secondpartition of the group of partitions, and so on, until all of therecords from the chunks of data are assigned to partitions. As such, theworker node 3306 can use or fill partitions of a particular group ofpartitions sequentially. Accordingly, records received during a firsttime and assigned to a particular group of partitions may be assigned toa first partition of the particular group of partitions and recordsreceived during a second time and assigned to the particular group ofpartitions may be assigned to a second partition of the particular groupof partitions.

As described herein, it will be understood that a different number ofrecords can be assigned to different record groups (and thereforedifferent groups of partitions). Accordingly, different groups ofpartitions can have a different number of partitions and can store adifferent number of records.

At block 6508, the worker node 3306 combines records of a particularpartition. As described herein, the worker node 3306 can combine recordsof a particular partition as records are received or once the partitionis filled. In some cases, the worker node 3306 combines similar records,such as records with at least one same keyword value or field value. Incertain cases, the worker node 3306 combines records by aggregating thefield value (e.g., a count) of one record with the field value (e.g.,count) of a similar record.

In cases where a field values are aggregated and used to combine similarrecords, it will be understood that, in some cases, one field value canbe used to determine whether records are similar, and another fieldvalue can be used in the aggregation process. For example, a sourcetypefield value can be used to identify similar records and a count field oraverage field can be used in the aggregation process. In some suchcases, the field value used to identify similar records can be an eventfield value corresponding to a field value of one or more events and thefield value used in the aggregation process can be a generated fieldvalue generated during query execution. However, it will be understoodthat various types of fields and field values can be used to identifysimilar records and/or used in the aggregation process.

At block 6510, the worker node 3306 processes the partition. In someembodiments, the worker node 3306 processes the partition based on thequery. For example, the worker node 3306 can perform one or moretransformations on the records based on the query, etc.

It will be understood that fewer, more, or different blocks can be usedas part of the routine 6500. For example, the routine 6500 may omitblocks 6502 and 6510 or omit block 6506 or 6508. As another example, theroutine 6500 can include obtaining additional chunks of datacorresponding to the same or different indexers 206 and processing theadditional chunks of data as described herein with reference to blocks6504, 6506, and/or 6508. Further, it will be understood that the routine6500 can be concurrently implemented by multiple worker nodes 3306receiving chunks of data from multiple indexers 206. In certainembodiments, the routine 6500 can include replacing one partition of agroup of partitions that is filled with records with another partitionof the group of partitions or assigning one or more first records of achunk of data to a first partition of the group of partitions andassigning one or more second records of the chunk of data to a secondpartition of the group of partitions. As described herein, the recordscan be assigned to different partitions based at least in part on thetime at which they are assigned by the worker node 3306.

In some embodiments, the routine 6500 can include reassigning records topartitions of a group of partitions. For example, as described herein,the routine 6500 can include reassigning records within a group ofpartitions so that similar records are assigned to the same partition.In certain embodiments, after reassigning records within the group, theworker node 3306 can combine records within the partitions. For example,the worker node 3306 can combine similar records that were assigned tothe same partition.

Moreover, it will be understood that one or more blocks described hereinwith reference to routine 6500 can be combined with one or more blocksof other routines described herein, such as the routines describedherein at least with reference to FIGS. 5, 6, 23-26, 31, 34, 38-45, 47,49, 52-57, 63, 66-69, 71, and 73 . Furthermore, it will be understoodthat the various blocks described herein with reference to FIG. 65 canbe implemented in a variety of orders. For example, blocks 6506 and 6508can be implemented concurrently, etc.

37.0. Estimating Generated Records

In some cases, it may be administratively easier to equally allocatecompute resources to execute queries. For example, the system 16 canallocate the same number of processors of an indexer 206, the samenumber of worker nodes 3306, and the same amount of memory to execute aquery. In some such cases, an equal distribution of compute resourcescan also make it easier to determine the number of queries that can beconcurrently executed. However, equally allocating compute resources toqueries can result in underutilized compute resources and/or the system16 being unable to execute certain queries. For example, computeresources may be allocated for, but used for only a portion of, a queryexecution. During the time that the compute resources are not used, thesystem 16 may not be able to be reallocate them to another query untilthe first query to which they are allocated is finished. Conversely, ifthe system 16 allocates a set number of worker nodes 3306 or partitionsto execute a query, but the number of records generated during the queryexecution exceed the capabilities of the allocated compute resources,then the system 16 may return inaccurate results or terminate the queryexecution before completion.

To address these and other potential issues, the system 16 can determinethe number of records being processed at each stage of the query toensure that sufficient resources are allocated to handle the largestnumber of records. In some embodiments, the system 16 can allocate anddeallocate compute resources at different stages of the query such thatthere are sufficient compute resources to execute the largest stage(stage with the largest number of records or that requires the mostprocessing) and that unused compute resources in other stages of thequery may be allocated to other queries. In this way, the system 16 canincrease the likelihood that sufficient resources are allocated toexecute the query, reduce query execution errors, and increase thethroughput of the system by increasing the total number of queries beingexecuted thereon.

In some embodiments, the system 16 can, as it processes the queryidentify the various processing tasks to be performed on the set of dataidentified by the query. For example, the system 16 can identify one ormore transforms or extraction rules that are to be applied to the set ofdata. For each processing task identified, the system can determine anumber of records generated by the respective processing task. In someembodiments, the system 16 can determine the number of records generatedby the processing task based on the identity of the processing task,and/or certain information about the records to be processed (e.g.,number of records, index, time range, sourcetype, etc.). Moreover, thedetermined number of records generated by a first processing task can beused to determine the number of records generated by a subsequent task(e.g., used as the number of records to be processed by the subsequenttask). Using this information, the system 16 can determine whichprocessing tasks generate the most records and allocate sufficientresources to hold/store the results as well as sufficient resources toexecute the processing task(s).

In some embodiments, the system 16 can use a priority level to determinethe amount of resources to allocate for the query. For example, thesystem can allocate additional resources for queries that have a higherpriority level and fewer resources for queries that have a lowerpriority level. In this way, the system 16 can provide different levelsof service based on the priority of a particular query. The prioritylevel can be determined based on the number of records ingested by oneor more worker nodes 3306 from one or more indexers 206, an indicationby a user entering the query, a user identifier (e.g., queries from oneuser can be identified as having a higher priority level than queriesfrom other users), time of day, etc.

In addition to allocating resources, the system can use the determinednumber of records from the different transforms to determine anexecution time of the query. For example, based on the number of recordprocessed/generated by each processing task and the amount of resourcesallocated to execute the processing task, the system 16 can determine anestimated execution time for that task. In some cases, the sum of theestimate execution time for each of the processing tasks can be used toestimate the total execution time of the query or at least a portion ofthe query, such as the portion of the query assigned to the one or moreworker nodes 3306.

In some embodiments, different processing tasks and sets of data, etc.can generate significantly different numbers of records. For example,applying a particular rule or transform to data from index 1, havingsourcetype 1, and within time range 1, can generate one number ofrecords, while data from the same index and having the same sourcetypeover a different time range can generate a very different number ofrecords. Similarly, applying the same rule or transform to data from adifferent index or sourcetype can result in a different number ofrecords being generated. In addition, different rules or transformsapplied to the same data can result in a significantly different numberof records being generated. Further, determining the records generatedfrom each processing task of a query as the query is being processed (orexecuted) can delay the query execution or significantly increase theexecution time of the query.

Accordingly, in some embodiments, the system 16 can determine a recordgeneration estimate for various sets of data. In some cases, the recordgeneration estimate can be generated before a query is received,processed, or executed, and can be used by the system 16 during a queryprocessing stage to determine the number of records generated bydifferent processing tasks of the query. In turn, the system 16 can usethe determined number of generated records, to allocate resources forthe query and/or estimate an execution time of individual processingtasks and/or the query (or portion thereof).

In certain embodiments, to determine the record generation estimate, thesystem 16 can obtain a sample set of data, apply a processing task tothe sample set of data, determine the record generation estimate basedon the results generated by the processing task, and store the recordgeneration estimate for use with queries that include the processingtask and/or use data that is similar to the sample set of data. In somecases, the sample set of data can correspond to data within a particulartime range that is obtained from a particular index, and/or has aparticular sourcetype.

Further, the system 16 can generate a record generation estimate fordifferent sets of data by varying the sample set of data. For example,the system 16 can apply the same processing task or rule to data fromdifferent indexes and time ranges to determine a record generationestimate for different combinations of the processing task, indexes, andtime ranges. Similarly, the system 16 can apply different processingtasks to the same sample set of data to determine different recordgeneration estimates based on the processing task applied to the sampleset of data. In certain embodiments, the record generation estimate cancorrespond to a ratio of the quantity of records generated by applyingthe processing task to the sample set of data and the quantity ofrecords of the sample set of data.

In some embodiments, the system 16 can store multiple record generationestimates. For example, the system can store the record generationestimates in one or more lookup tables and/or one or more configurationfiles. In certain cases, the system 16 can store different recordgeneration estimates based on different combinations of indexes, timeranges, source types, processing tasks, etc. A non-limiting example mayinclude:

Record Time Rule/Processing Generation Index Range Sourcetype TaskEstimate main T0-T1 apache_error Rule 1 2.3 main T1-T2 apache_error Rule1 1.6 main T0-T1 access_combined Rule 2 3.1 main T1-T2 access_combinedRule 2 1.3 _test T0-T1 apache_error Rule 1 2.4 _test T0-T1 apache_errorRule 1 2.7 _test T0-T1 access_combined Rule 2 3.5 _test T1-T2access_combined Rule 2 1.9

As shown in the example table above, a configuration file can includedifferent record generation estimates based on different indexes, timeranges, sourcetypes, and/or rules/processing tasks. In certainembodiments, the record generation estimate can correspond to a ratio ofrecords generated to records processed using the processing task. Insome cases, overlapping time ranges can be used. For example, one timerange might be time T0-T3 and another time range (for the same ordifferent data) can be time T1-T2 or T1-T4, etc. Further, in theillustrated embodiment, a different rule is used for the differentsourcetypes. However, it will be understood that in certain embodiments,the same rule may be used for different sourcetypes, etc.

Although not shown in the table above, it will be understood that insome cases, the configuration file can include a record generationestimate corresponding to a sequence of different processing tasksapplied to a sample set of data. In some such embodiments, the recordgeneration estimate associated with the sequence of different processingtasks can be used to allocate resources for the sequence of processingtasks and/or estimate an execution time of the sequence of processingtasks.

In certain embodiments, the rule itself may include certain parameters,such as an index and/or sourcetype. As a non-limiting example, where arule identifies a particular index and sourcetype, the configurationfile may include fewer fields, such as the following:

Time Rule/Processing Record Generation Range Task Estimate T0-T1 Rule 12.3 T1-T2 Rule 1 1.6 T0-T1 Rule 2 3.1 T1-T2 Rule 2 1.3 T0-T1 Rule 3 2.4T0-T1 Rule 3 2.7 T0-T1 Rule 4 3.5 T1-T2 Rule 4 1.9

During query processing, the system 16 can use query parameters toidentify the record generation estimate for a particular processingtask. For example, the system 16 can use a time range associated with arule or processing task to determine the record generation estimate forrecords within the specified time frame that are processed according tothe identified rule. Using the number of records to be processed and therecord generation estimate, the system 16 can determine an estimatednumber of records that will be generated by the processing task. Forexample, based on the number of records to be processed by theprocessing task and the record generation estimate, the system 16 candetermine (or estimate) the number of records that the processing taskwill generate during query execution.

FIG. 66 is a flow diagram illustrative of an embodiment of a routine6600 implemented by a search head 210 to allocate resources and/orestimate execution time based on records generated during a processingtask. Although certain blocks are described as being implemented by asearch head 210, it will be understood that the elements outlined forroutine 6600 can be implemented by one or more computingdevices/components (alone or in combination) that are associated with adata intake and query system 16, such as the worker node 3306, searchprocess master 3302, the query coordinator 3304, etc. Thus, thefollowing illustrative embodiment should not be construed as limiting.Moreover, it will be understood that routine 6600 is not limited to adata intake and query system 16, but can be used to allocate resourcesand/or estimate the execution time of various records by one or moreprocessors of a distributed execution environment.

At block 6602, the system 16 receives a query. As described herein, thequery can identify a set of data and a manner for processing the set ofdata, identify one or more dataset sources for obtaining the data,include one or more commands for executing a portion of the query by oneor more indexers 206, one or more commands for executing a portion ofthe query by one or more worker nodes 3306 or by an external data system12, etc. In certain embodiments, the search head 210 (or querycoordinator 3304) can process the query and identify one or moresubqueries (or portions of the query) for execution by one or moreindexers 206, one or more worker nodes 3306, and/or an external datasystem 12. In some such embodiments, the search head 210 may performblock 6604, 6606, and/or 6608 for all or a subset of the identifiedsubqueries (or portions of the query).

At block 6604, the search head 210 identifies a processing task. Incertain embodiments, the search head 210 can identify the processingtask by parsing the query. For example, the search head 210 can identifyone or more processing tasks based on a command identified in the query,etc. The command may be a processing task and/or may refer to,reference, or rely on a processing task. The processing task can, insome cases, correspond to a rule, such as an extraction rule, to beapplied to at least a portion of the set of data of the query, such asan extraction rule, or a transform that transforms at least a portion ofthe set of data. Furthermore, the processing task can correspond to aprocessing task performed by one or more indexers 206, by one or moreworker nodes 3306, and/or by another component of the system 16.

At block 6606, the search head 210 determines records generated by theprocessing task. In some embodiments, the records generated by theprocessing task can correspond to an estimated number of records thatwill be output (or generated) in response to the processing task. Incertain embodiments, the search head 210 can determine the recordsgenerated by the processing task based on one or more of the number ofrecords processed according (or input into) the processing task, theidentification of the processing task, a time range of the recordsprocessed according to the processing task, a sourcetype of the recordsprocessed according to the processing task, an index associated with therecords processed according to the processing task, and/or otherinformation.

In some embodiments, the search head 210 can determine the number ofrecords to be processed by the processing task based on the query and/orbased on an estimated number of records received from the one or moreindexers. For example, the processing task may be the first processingtask of the query or a first processing task executed by the one or moreworker nodes 3306 after receiving chunks of data from the one or moreindexers 206. In some such cases, the processing task may be applied toall or a subset of the set of data identified by the query. As describedherein, the query can include one or more parameters to identify a setof data to be processed. In some such embodiments, the search head 210can use the query parameters used to identify the set of data, such as,but not limited to, a time range, index, host, source, sourcetype,keyword, field, field value, etc., to determine the number of records tobe processed by the processing task.

In certain embodiments, identifying the records to be processed by aparticular processing task can be similar to the processes describedherein at least with reference to blocks 6304 and 6306 of FIG. 63 . Forexample, based on the query, the search head 210 can identify bucketsassociated with the query and bucket data of the buckets associated withthe query. In some cases, this can include events or records thatsatisfy the query parameters. The search head 210 can determine that thenumber records or events that satisfy the query parameters correspond tothe number of records to be processed by a particular processing task.In certain embodiments, the search head 210 can use an inverted index todetermine the number of records to be processed by the processing task.

In some embodiments, the search head 210 can determine the number ofrecords to be processed by the processing task based on the number ofrecords generated by a preceding processing task. For example, if theprocessing task processes data after another processing task, therecords output by the first processing task can be used to determine thenumber of records to be processed by the second processing task.

Similarly, a number of methods can be used to determine the number ofrecords generated by a processing task. In certain embodiments, asdescribed herein, the search head 210 can use a lookup table orconfiguration file to determine the number of records generated by theprocessing task. In some cases, the search head 210 can use certaincharacteristics of the records or processing task (e.g., time range,sourcetype, and/or index of records and/or identity of processing task)to obtain a record generation estimate from a lookup table orconfiguration file that stores this information for multiplecombinations of processing tasks and chunks of data. For example, theconfiguration file can store multiple record generation estimates andfor each record generation estimate, the configuration file can store atime range, sourcetype, index, processing task, etc. associated withthat record generation estimate. The search head 210 can use the recordgeneration estimate to determine the number of records generated by theprocessing task. In certain embodiments, the search head 210 canmultiply the number of records received or processed by the processingtask by the record generation estimate to determine the number ofrecords generated by the processing task for the query.

In certain embodiments, the search head 210 can determine the recordsgenerated by the processing task in response to receiving the query. Insome such embodiments, the search head 210 can obtain a sample data setthat is similar to the set of data that is to be processed according tothe processing task (e.g., similar or same time range, index, and/orsourcetype, etc.), apply the processing task to the sample set of data,determine a record generation estimate based on the number of recordsgenerated from the sample set of data, and use the record generationestimate to determine the records generated by the processing task forthe query. In some embodiments, the search head 210 can determine therecords generated by the query based on a user provided estimate orvalue.

At block 6608, the search head 210 allocates compute resources and/orestimates execution time for the processing task based on the determinedrecords. In some embodiments, the search head 210 can allocate resourcesby allocating worker nodes 3306, processors of worker nodes 3306, coresof indexers 206, execution resources of cores, etc. Accordingly, in someembodiments, based on the determined records generated by the processingtask, the search head 210 can allocate one or more processors or workernodes 3306 to execute the query.

In certain embodiments, the search head 210 can use a variety of rangesto assign a size category to the determined records generated by theprocessing task. For example, the search head 210 can categorize thedetermined records generated by the processing task as in the millions,billions, or trillions. Based on the determined category, the searchhead 210 can allocate compute resources. For example, the search head210 can allocate more compute resources for queries that includeprocessing tasks that generate trillions of records and fewer computeresources for query with processing tasks that generate billions ormillions of records. It will be understood that the ranges and sizecategories can be as coarse or granular as desired. For example, thesearch head 210 can include a different size category for each thousand,million, 10 million, 100 million, billion, 10 billion, and so on recordsas desired.

In some embodiments, the search head 210 can allocate the computeresources based on a priority level or prioritization factor of thequery. For example, the search head 210 can allocate more computeresources for queries with a higher priority level or prioritizationfactor and less compute resources for queries with a lower prioritylevel or prioritization factor. As described herein, the priority levelcan be based on one or more factors, such as a user identifier, querysize, user indication, etc.

In some embodiments, the search 210 can allocate compute resources basedon a combination of size category and priority level. For example,different amount of compute resources can be allocated to process taskswith a particular size category depending on the priority level.Accordingly, the search head 210 can use a variety of factors toallocate compute resources to the processing task and/or query.

In certain embodiments, the search head 210 can estimate an executiontime for the processing task based on the number of recordsprocessed/generated and the allocation of compute resources. In certainembodiments, the search head 210 can store execution time estimates fordifferent sets of data. In some cases, the execution time estimates canbe stored in a configuration file with the record generation estimate.The execution time estimate can indicate an amount of time for onecompute resource (or more) to process a record or a set of records(e.g., 1,000 records, 10,000 records) that are similar to the records tobe processed according to the processing task. Using the identificationof the processing task, the determined records generated by theprocessing task, and the compute resources allocated to the processingtask, the search head 210 can estimate the execution time of theprocessing task. Further, the search head 210 can provide the executiontime estimate to a user and/or use the execution time estimate todetermine an execution time estimate of the entire query. In someembodiments, if the execution time estimate does not satisfy anexecution time threshold, the search head 210 can alert a user and/orallocate additional compute resources to the query to satisfy theexecution time threshold. For example, the search head 210 can increasethe number of worker nodes 3306 allocated to execute the query. In somecases, the execution time threshold can be based on user input, prioritylevel of the query, or other information.

It will be understood that fewer, more, or different blocks can be usedas part of the routine 6600. For example, the routine 6600 may omitblock 6602. As another example, the routine 6600 can include identifyingadditional processing tasks of the query (or a subquery of the query)and determining records generated by the additional processing tasks. Incertain embodiments, the search head 210 performs blocks 6604 and 6606for a subset of the processing tasks of a query. For example, the searchhead 210 may perform blocks 6604 and 6606 for processing tasks to beperformed by the worker nodes 3306 but not the indexers 206 (or viceversa). In some cases, some processing tasks of the query may generatemore records than it receives, while other processing tasks may reducethe number of records. Accordingly, in some embodiments, the search head210 may perform blocks 6604 and 6606 for the processing tasks identifiedas generating more records than are received, but may or may not performblocks 6604 and 6606 for the processing tasks identified as reducing thenumber of records.

In certain embodiments, the search head 210 can allocate resources forall or a group of processing tasks of the query based on the recordsgenerated by one or more of the processing tasks of the query. Forexample, the search head 210 can allocate resources for some or allprocessing tasks of the query based on the processing task of the querythat generates the most number of records. In this way, the search 210can increase the likelihood that sufficient resources are allocated toprocess the query.

In some embodiments, the search head 210 can individually allocatecompute resources for each processing task. For example, the search head210 can allocate a first set of compute resources for a first processingtask and allocate a second set of compute resources for a secondprocessing task. As described herein, in some embodiments, theallocation of compute resources to each task can be based on the numberof records processed/generated by the processing task and/or a prioritylevel. In some cases, the search head 210 can use a different prioritylevel for different processing tasks.

In certain embodiments, the search head 210 can determine an executiontime estimate for each processing task. In some cases, the sum of theexecution time estimates can be used to estimate the query executiontime. In certain embodiments, the processing task that takes the longesttime can be used to estimate the query execution time. In certainembodiments, the sum of some processing tasks and the longest time ofother processing tasks can be used together to determine the queryexecution time.

In some cases, the system 16 can initiate execution of the query basedon the resources allocated to the query. During the query execution, thesystem 16 can monitor the execution of the query and update estimates orconfiguration files based on the monitoring. For example, the system 16can determine the actual number of records generated by each processingtask and update a corresponding lookup table or configuration file basedon the actual number of records generated. In some such cases, thesystem 16 can replace a record generation estimate with the determinedrecord generation, create a separate record for the determined recordgeneration (with corresponding information, such as, index, processingtask, sourcetype, etc.) and/or add the determined record generation tothe record generation estimate. The updated configuration file can beused to determine records generated by processing tasks for futurequeries. Similarly, the system 16 can monitor the execution time of eachprocessing task and update a lookup table and/or configuration filebased on the monitoring. Accordingly, as queries are executed, thesystem 16 can update its record generation estimates and/or executiontime estimates to improve estimates for future queries.

Moreover, it will be understood that one or more blocks described hereinwith reference to routine 6600 can be combined with one or more blocksof other routines described herein, such as the routines describedherein at least with reference to FIGS. 5, 6, 23-26, 31, 34, 38-45, 47,49, 52-57, 63, 65, 67-69, 71, and 73 . In certain embodiments, any oneor any combination of 6602-6608 can be part of a query processing stage,as described herein. Furthermore, it will be understood that the variousblocks described herein with reference to FIG. 66 can be implemented ina variety of orders. For example, blocks 6604-6608 can be implementedconcurrently, etc.

As described herein, processing tasks can modify the data in such a wayso as to increase (or decrease) the number of records. For example, onerecord of a set of data can turn into multiple records after beingprocessed according to the processing task. In some embodiments, onerecord can turn into thousands or even millions of records after beingprocessed according to a processing task. Furthermore, it can bedifficult to predict the number of records generated by a particularprocessing task on a particular set of data. Moreover, if a processingtask results in more records than expected by the system 16, the system16 may be unable to complete the execution of the query or the system 16may use significantly more time than expected to execute the query.

FIG. 67 is a flow diagram illustrative of an embodiment of a routine6700 implemented by a search head 210 to determine a record generationestimate. In certain cases, the routine 6700 can be executed before aquery is received or processed or during query processing or execution.In some cases, the routine 6700 can be executed when a transform,extraction rule, or other processing task is added to the system 16and/or when additional data is stored.

Although certain blocks are described as being implemented by a searchhead 210, it will be understood that the elements outlined for routine6700 can be implemented by one or more computing devices/components(alone or in combination) that are associated with a data intake andquery system 16, such as the worker node 3306, search process master3302, the query coordinator 3304, etc. Thus, the following illustrativeembodiment should not be construed as limiting. Moreover, it will beunderstood that routine 6700 is not limited to a data intake and querysystem 16, but can be used to determine a record generation estimate ina variety of systems and environments.

At block 6702, the search head 210 obtains a sample set of data. Thesample set of data can be identified and obtained based on one or moredata criteria. For example, the sample set of data can be identified andretrieved based on a determined range of time and index (or data storepartition). However, it will be understood that fewer, more, ordifferent data criteria can be used to identify the sample set of data.For example, the sample set of data can be identified based on one ormore fields, field values, keywords, a host, source, or sourcetype,indexer 206, etc. In some embodiments, the sample set of data can beretrieved in a manner similar to the manner in which data is retrievedas part of executing a query, as described herein.

In certain cases, the sample set of data can correspond to data from oneor more buckets of an indexer 206 and/or common storage 4602. In certainembodiments, the sample set of data can correspond to a chunk of datacomprising 50,000 or 100,000 records or events. In some cases, the chunkof data can include events/records with the same or different sourcetypeor other matching field values. It will be understood that the chunks ofdata can include fewer or more records.

At block 6704, the search head 210 applies a processing task to thesample set of data. As described herein, a processing task cancorrespond to one or more operations to be applied to data. In somecases, a processing task can be stored as an extraction rule ortransform for certain data, such as data from a particular index orhaving a particular sourcetype, etc. Accordingly, in some embodiments,the search head 210 can apply the processing task to the sample set ofdata by applying an extraction rule to the sample set of data ortransforming the sample set of data.

At block 6706, the search head 210 determines a number of recordsgenerated by applying the processing task to the sample set of data. Asdescribed herein, each processing task can generate a different numberof records from different sets of data. Accordingly, the search head 210can determine the number of records generated by applying processingtask to the sample set of data.

At block 6708, the search head 210 determines a record generationestimate. In some embodiments, the record generation estimate is basedon the number of records generated and the number of events/records ofthe sample set of data processed using the processing task. In someembodiments, all events/records of the sample set of data are processedusing the processing task. In certain embodiments, a subset ofevents/records of the sample set of data are processed using theprocessing task. For example, the sample set of data may include a setof events from a particular index within a time range without regard tosourcetype. In the event the processing task applies to a particularsourcetype, then the processing task may not be applied to all of theevents/records of the sample set of data. As another example, if theprocessing task applies to all events of the particular index or thesample set of data only includes data from the particular sourcetype,then the processing task may be applied to all events/records of thesample set of data.

In certain embodiments, the record generation estimate can correspond toa ratio of the records generated and the number of events/recordsprocessed by the processing task. In some embodiments, the recordgeneration estimate can correspond to a ratio of the records generatedand the number of events/records of the sample set of data. In someembodiments, the record generation estimate can correspond to anestimated number of records generated from each event/record of thesample set of data.

It will be understood that fewer, more, or different blocks can be usedas part of the routine 6700. For example, the routine 6700 can includeapplying distinct processing tasks to the sample set of data todetermine multiple record generation estimates for each processing task.In some embodiments, the routine 6700 can include sequentially applyingmultiple distinct processing tasks to the sample set of data anddetermining a record generation estimate for the sequence of processingtasks.

In certain embodiments, the routine 6700 can include applying the sameor different processing tasks to different sample sets of data todetermine additional record generation estimates. Further, in somecases, the search head 210 can perform routine 6700 at a predeterminedinterval or frequency, or as new data is received, or when a newprocessing task is added, such as a new extraction rule to aconfiguration file.

In some embodiments for each unique combination of sample set of dataand processing task, the search head 210 can store the determined recordgeneration estimate. In some embodiments, the record generationestimates can be stored in a lookup table or configuration file. Incertain embodiments, some or all of the entries of the lookup table orconfiguration file can include an identification of the processing taskand one or more characteristics of the sample set of data. For example,the lookup table or configuration file can include a time range of thesample set of data, an index of the sample set of data, a sourcetype ofthe sample set of data, one or more other field values or keyword valuesas desired. In certain embodiments, an entry of the lookup table orconfiguration file includes only a time range, an identification of theprocessing task, and the corresponding record generation estimate.

In addition, as described herein, the record generation estimates (orcorresponding lookup tables or configuration files) can be used toallocate compute resources for a particular processing task or queryand/or used to estimate the execution time of the particular processingtask or the query as a whole.

Moreover, it will be understood that one or more blocks described hereinwith reference to routine 6700 can be combined with one or more blocksof other routines described herein, such as the routines describedherein at least with reference to FIGS. 5, 6, 23-26, 31, 34, 38-45, 47,49, 52-57, 63, 65, 66, 68, 69, 71, and 73 . Furthermore, it will beunderstood that the various blocks described herein with reference toFIG. 67 can be implemented in a variety of orders. For example, blocks6704-6708 can be implemented concurrently, etc.

38.0. Query-Resource Allocation and Concurrency

As described herein, the amount of execution resources to executedifferent queries can vary significantly. For example, some queries mayuse one processing pipeline of one indexer 206 and one processingpipelines of a search head 210 to execute, whereas another query may useseveral pipelines on multiple indexers 206, multiple worker nodes 3306(each with multiple processing cores), pipelines or cores from one ormore external data systems 12, as well as one or more pipelines of asearch head 210. Given the difference in execution resources used byeach query, it can be difficult to determine whether the data intake andquery system 16 has sufficient resources to execute a particular querywhen it is received. For example, in some cases, the system 16 may beginexecuting the query using execution resources of one or more indexers206. However, the worker nodes 3306 may have insufficient computeresources to receive and process the results from the indexers 206. Insome such cases, the system 16 may terminate the query or wait until theworker nodes 3306 become available. However, during that time, theexecution resources of the indexers 206 may not be allocated to executeother queries. Further, the system 16 may wait for an undeterminedamount of time before the worker nodes 3306 become available.

To address these and other potential issues, the system 16 can track theexecution resources of its various components. As new queries arereceived, the system 16 can determine a query-resource allocation or theamount of execution resources of the different components of the system16 to use to execute the query. The system can compare thequery-resource allocation with the amount of execution resourcesavailable from the different components. If there are sufficientexecution resources available from the various components, the system 16can execute the query. If not, the system can place the query in a queuefor later execution. Moreover, as described herein, based on the size ofthe query and amount of execution resources to be allocated, the system16 can estimate a query execution time for the query. By schedulingqueries based on a determined query-resource allocation and theavailability of execution resources, the system 16 can be improved. Forexample, the system 16 can reduce the likelihood that there will beinsufficient execution resources to execute a query, improve utilizationof execution resources of the system 16 (e.g., increase the usage timeof the compute resources), etc. As such, the system 16 can increase thenumber of queries being executed over a period of time (e.g., increasethroughput of query executions) and decrease the wait time to executequeries.

As mentioned, the system 16 can track the total number of executionresources of individual components, as well as the total number ofexecution resources of the system 16 as a whole the total number ofexecution resources of portions of the system 16, such as the group ofindexers 206 or group of worker nodes 3306. As queries are received, thesystem 16 can determine the number of execution resources to be used byindividual components and the system 16 as a whole to execute the queryand/or identify portions of the query and a query-resource allocationfor each portion of the query. The system 16 can deduct the number ofexecution resources for the query from the total number of resources aswell as from the individual resources.

In some embodiments, the system 16 can calculate a number of individualand indexer-wide execution resources based on the number of processorcores of each indexer 206. For example, the system 16 may determine thateach processor core can handle a certain number of queries (orpipelines) and/or that an individual indexer 206 can handle a certainnumber of queries (or pipelines) per core plus an additional amount. Insome such embodiments, the system 16 can determine that the executionresources of one indexer 206 is number of cores*queries supported percore+offset. In some cases, the system 16 can determine that eachprocessor can support one query from each search head 210 of a searchhead cluster. Thus, the number of queries supported by each core (andindexer 206) or the number of execution resources of each core can bebased on the number of search heads 210 in a search head cluster of thesystem 16. In certain cases, the system 16 can sum the executionresources from all indexers 206 to determine the total number of indexer206 execution resources.

In certain embodiments, the system 16 can perform a similardetermination with the worker nodes 3306. For example, the system 16 candetermine a number of queries that individual cores of a worker node3306 can support, a number of queries that each worker node 3306 cansupport, and/or the number of queries that the worker nodes 3306together can support. In some embodiments, the system 16 can specify acertain number of worker nodes 3306 to be allocated for each query thatinvolves worker nodes 3306 or a total number of queries involving workernodes 3306 in the aggregate that are supported by the system 16. In somesuch embodiments, when a query involving worker nodes 3306 is received,the system 16 can reduce the count of additional queries that can useworker nodes 3306.

In addition to tracking the number of execution resources of thedifferent components of the system 16, the system 16 can determine aquery-resource allocation for queries to be executed by the system. Asmentioned, the query-resource allocation can vary depending on the typeof query, the query size (amount of events or records to be processed),the components being used, etc. For example, queries that only useindexers 206 to execute the query may have one query-resource allocationand queries that use worker nodes 3306 (alone or in conjunction withindexers 206) may have a different query-resource allocation.

In addition, the type of query to be executed can affect how the searchhead 210 allocates execution resources from one or more indexers 206.For example, one type of query (also referred to herein as an “indexersearch”) uses execution resources of one set of indexers 206 to obtainthe set of data, perform some processing, and return the partial resultsto a search head 210, which may perform some additional processing onthe data.

In some embodiments, indexer searches can use a static number ofexecution resources of each indexer 206 involved in the query. Forexample, each indexer 206 may allocate one execution resource (orpipeline) for each indexer search. Thus, if only one indexer 206 storesor has access to the set of data of the query only one executionresource of the indexer 206 may be allocated or used for the search.Accordingly, in certain embodiments, to determine a query-resourceallocation for an indexer search, the system 16 can use the total numberof indexers 206 to be used for the query and the number of executionresources allocated from each indexer 206.

Another type of query uses multiple sets of indexers 206 to execute aquery (also referred to herein as an “intermediary search”). A first setof execution resources of indexers 206 is used to obtain the set of dataof the query and may perform some processing on the set of data. One ormore second sets of execution resources of the indexers 206 collateand/or perform additional processing on data obtained by the first setof indexers 206, and provide the results to the search head 210. In somesuch embodiments, the second set(s) of execution resources of theindexers 206 can act as intermediaries for the first set of executionresources to the search head 210. In some cases, the second set ofexecution resources that receive data from the first set of executionresources can be a subset of the first set of execution resources (orvice versa) or a set of other execution resources (from the same ordifferent indexers 206).

In certain embodiments, intermediary searches may use at least the samenumber of execution resources of indexers 206 as a corresponding indexersearch plus additional execution resources for the one or more secondsets of execution resources (e.g., to collate the partial results fromthe first set of execution resources, further process the data, and/orfunction as the intermediaries to the search head 210). In some suchcases, the total number of execution resources used for an intermediarysearch used may correspond to the total number of execution resourcesused by the first set of execution resources (execution resources usedto obtain the first set of data) or the number of indexers used as partof the query plus an additional amount.

In certain cases, the “additional amount” of execution resources can bedetermined based on a weighting factor. The weighting factor cancorrespond to the number, ratio, or amount of additional executionresources to be allocated to collate or further process data from thefirst set of execution resources, or otherwise act as an intermediary tothe search head 210. In some cases, the additional execution resourcescan be a subset of the first set of execution resources. In someembodiments, the system 16 can determine the total amount of executionresources from the indexers 206 for an intermediary search by increasingor multiplying the number of execution resources used by the first setof execution resources by the weighting factor. For example, if aweighting factor is 40% or 1.4 and one execution resource from each often indexers 206 will be used to obtain the initial set of data, thenthe system 16 can allocate fourteen execution resources of the indexers206 for the query-resource allocation. In certain embodiments, thesystem 16 can round the determined number of execution resources, use aceiling, or floor, as desired, to determine the total number ofexecution resources to be allocated. For example, if six executionresources are to be used by the first set of indexers 206 and theweighting factor is 40%, then the system 16 can determine that thequery-resource allocation to be 8 (floor or rounding) or 9 (ceiling)execution resources.

In some embodiments, the system 16 can use the same weighting factor forall intermediary searches. In certain embodiments, the system 16 can usea different weighting factor for intermediary searches depending on thesize of the set of data, a prioritization factor, the amount ofprocessing to be done on the set of data, one or more query parametersor one or more characteristics of the data, such as, but not limited to,host, source, sourcetype, time range, index, fields, field values,keywords, etc. For example, the system 16 can use a larger weightingfactor for larger sets of data (or sets of data that involve a largenumber of events or records) or queries with a higher prioritizationfactor, queries with more processing to be done, queries that referencea particular host, source, sourcetype, index, or keyword or field value,etc. Accordingly, in certain embodiments, to determine a query-resourceallocation for an indexer search, the system 16 can use the total numberof execution resources to be used to obtain the set of data and aweighting factor.

A third type of query can use worker nodes 3306 to process data (alsoreferred to herein as a “worker node search”). For example, executing aworker node search may involve using execution resources of a set ofindexers 206 of the system 16 to obtain a set of data, perform someprocessing, and export the data to worker nodes 3306. The worker nodes3306 may perform additional processing based on the query and providethe results of the processing to the search head 210. In certainembodiments, some worker node searches may not use the executionresources of the indexers 206 of the system 16. In some suchembodiments, the worker nodes 3306 may obtain data from one or moreexternal data systems 16 (which may or may not include indexers 206),process the data, and provide the results of the processing to thesearch head 210.

Given that worker node searches can involve allocating executionresources from the indexers 206 and compute resources of the workernodes 3306, the system 16 can determine a query-resource allocation forthe different portions of the worker node search. For example, thesystem 16 can determine a query-resource allocation for the indexerportion of the worker node search and a query-resource allocation forthe worker node portion of the worker node search.

The system 16 can use a variety of techniques to determine thequery-resource allocation for the indexer portion of a worker nodesearch. In certain embodiments, the system 16 can use a fixed number ofexecution resources from each indexer 206 used in the query to determinea query-resource allocation. In some embodiments, the system 16 can usean execution resource allocation policy. As described herein, at leastwith reference to FIGS. 62A, 62B, and 63 , the execution resourceallocation policy can use a variety of factors to determine the numberof execution resources to allocate. For example, the execution resourceallocation policy can use the number of buckets, amount of bucket data,number of available resources, a threshold number of executionresources, the number of worker nodes 3306 or number of processors ofworker nodes 3306, etc., to determine the query-resource allocation forthe indexer portion of a worker node search.

For worker node portions of a query, the system 16 can use a variety oftechniques to determine the query-resource allocation. In some cases,the system 16 can assign the same quantity of worker nodes 3306 and/orcompute resources of the worker nodes 3306 for each query. In someembodiments, the system 16 can assign worker nodes 3306 and/or computeresources of the worker nodes 3306 based on the size of the query and/ora priority level. As described herein, in some embodiments the size ofthe query can correspond to a number of records to be processed, thesize of the records to be processed, and/or an amount of memory used tostore the records, etc. As described herein, at least with reference toFIG. 66 , the system 16 can determine the size of a query based on oneor more processing tasks of the query and/or a record generationestimate.

In some cases, the system can determine a query-resource allocation (ofthe indexers 206 and/or of the worker nodes 3306) based on a quantity ofthe events or records received and/or processed by the indexers 206and/or worker nodes 3306. For example, the system 16 can use one or moresize categories to categorize the amount of records/events to beprocessed by the indexers 206/worker nodes 3306. As described herein, atleast with reference to FIG. 69 , the size categories can be as coarseor granular as desired. For example, in some embodiments, the system 16can use three categories for queries that have millions (or less),billions, or trillions of events. As another example, the system 16 canhave a different size category for each million, ten million, hundredmillion, billion (or more) records. Based on the size category, thesystem 16 can allocate a different amount of worker nodes 3306, adifferent number of compute resources of worker nodes 3306, a differentnumber of compute resources for each worker node 3306 allocated, and/ora different number of execution resources of the indexers 206.

In some cases, the size category for the query can be based on the sizeof the largest number of records processed by or generated from aprocessing task of the query. In certain cases, the system 16 candynamically assign compute resources to processing tasks based on thenumber of records processed/generated by the processing task.Accordingly, in some embodiments, a different quantity of computeresources can be allocated to different portions of the query, and/orcompute resources can be dynamically allocated to different portions ofthe query.

In addition, the system 16 can determine a query-resource allocation (ofthe indexers 206 and/or worker nodes 3306) based on a prioritizationscheme or priority level. For example, queries as signed a higherpriority level or priority level can be allocated more compute resources(or execution resources) than queries assigned a lower priority level orpriority level. In some cases, the system 16 can determine the priorityor priority level based on the user initiating the query, a schedule,the data being queried (e.g. based on indexes, time ranges, sourcetypes,host, sources, indexers 206, etc.), etc. In some cases, the system 16can use a combination of query size and priority level to determine thequery-resource allocation for the query or for different portions of thequery. For example, the system can determine the size category of thesystem and within that size category determine the amount of resourcesto allocate based on the priority level. In some such cases, a differentsize category or priority level can result in a different number ofresources allocated. In certain embodiments, the system 16 can use thesame priority level for an entire query and/or for different portions orprocessing tasks of the query. As such, different compute resources orexecution resources can be allocated to different portions or processingtasks of the query.

In certain embodiments, the system 16 can determine the query-resourceallocation based on a query execution time threshold (e.g., amount oftime that the execution of the query should take) or query completiontime (e.g., time by which the query is to be completed). For example, auser or the query can indicate a time by which the query is to becompleted and/or an amount of time to execute the query. Based on theindicated time, the system 16 can allocate compute resources. Forexample, the system can allocate more compute resources to a query thatis to be executed in less time compared to the same query with anindication that it can be executed over a longer period of time or moreresources to a query that is to be completed sooner than to a query thatis to be completed later. In addition, in some cases, the system 16 canassign different priority levels to queries based on the query executiontime threshold or query completion time. In addition, as the completiontime nears, the system 16 can assign a higher priority to a query.

Accordingly, an indexer search may use a certain amount executionresources of the indexers 206, an intermediary search may use adifferent amount of execution resources of the indexers 206, and aworker node search may use another amount of (or no) execution resourcesof the indexers 206 and compute resources of the worker nodes 3306. Inaddition, different indexer searches may use a different number ofindexers 206 and/or execution resources of indexers 206 based on thequery, priority level, etc. Similarly, different intermediary searchesand worker node searches can use different amount of execution resourcesor compute resources depending on the query, priority level, individualprocessing tasks, etc.

Based on the availability of execution resources from the indexers 206and the worker nodes 3306 and the query-resource allocation, the system16 can determine whether a query can be executed at that time or whetherit should be placed in a queue for later execution. In addition, bydetermining a query execution time, as described herein, at least withreference to FIG. 69 , and determining execution resources fromdifferent components to use to execute the query, the system 16 candynamically schedule queries for execution. For example, based on aquery (or subquery) execution time estimate, and query-resourceallocation, the system 16 may determine that indexers 206, but notworker nodes 3306 are available to execute a query at time T0, and thatthe worker nodes 3306 will become available at time T1. Based on thatinformation and the system 16 determining that the subquery executiontime of the indexer portion of the query is T1, the system 16 can beginexecuting the indexer portion of the worker node search at time T0. Asthe indexer portion of the query will complete at T1, the system 16 canallocate the worker nodes 3306 by that time to continue processing thequery. In this way, the system 16 can increase throughput of queryexecution and reduce the waiting time for queries.

In some embodiments, the system 16 can dynamically allocate differentamounts of resources during query execution. For example, if the system16 receives a higher priority query and determines that there areinsufficient execution or compute resources to execute the higherpriority query using a first priority level, the system 16 can beginexecuting the higher priority query using a second priority level thatis lower than the first level or uses fewer execution or computeresources. If the system 16 determines that additional resources will bereceived during the execution of the query such that it can provide thefirst priority level, the system 16 can add those execution or computeresources during the execution of the query. The system can similarlydynamically allocate different amounts of execution or compute resourcesduring query execution for queries of different sizes or to moreefficiently manage a scheduling queue, etc. For example, the system 16can allocate additional resources to one query during execution tofinish it in less time so as to free up execution or compute resourcesfor more or larger queries that follow.

FIG. 68 is a flow diagram illustrative of an embodiment of a routine6800 implemented by a search head 210 to schedule a query. Althoughcertain blocks are described as being implemented by a search head 210,it will be understood that the elements outlined for routine 6800 can beimplemented by one or more computing devices/components (alone or incombination) that are associated with a data intake and query system 16,such as the worker node 3306, search process master 3302, the querycoordinator 3304, etc. Thus, the following illustrative embodimentshould not be construed as limiting. Moreover, it will be understoodthat routine 6800 is not limited to a data intake and query system 16,but can be used to queue execution tasks in a variety of systems andenvironments.

At block 6802, the search head 210 receives a query, as describedherein, at least with reference to block 6702 of FIG. 67 , FIG. 6 , andelsewhere. At block 6804, the search head 210 determines aquery-resource allocation for the query. In some embodiments, as part ofdetermining the query-resource allocation for the query, the system 16can determine a query-resource allocation for one or more portions ofthe query. The portions may refer to different sections of the query (orsubqueries generated from or referenced by the query) that are to beexecuted by different components of the data intake and query system 16(or an external data system 12) or to one or more subqueries of thequery (e.g., a portion of the query or a separate query referenced by oridentified by the query). For example, the system 16 can identify afirst query portion that is to be executed by one or more indexers 206(indexer portion), a second query portion that is to be executed by oneor more worker nodes 3306 (worker node portion), and a third portion tobe executed by a query coordinator 3305 and/or a search head 210(results portion). Other portions can be identified as well, such as oneor more portions to be executed by one or more external data systems 12(external data system portions). In some such cases, the system 16 candetermine a query-resource allocation for the different portions of thequery.

As described herein, the system can determine the query-resourceallocations for the different query portions in a variety of ways. Insome cases, the system 16 determines query-resource allocations fordifferent query portions in different ways. For example, the system 16can determine a query-resource allocation for an indexer portiondifferently than the way in which it determines a query-resourceallocation for a worker node portion.

In some embodiments, the system 16 can use the type of query (indexersearch, intermediary search, worker node search), the amount of thebucket data, the number of indexers 206, and/or a priority level todetermine the query-resource allocation for indexer search portions. Asdescribed herein, the system 16 can allocate more execution or computeresources for queries with the higher priority level than for querieswith the lower priority level.

As mentioned, the query type can affect the manner in which the system16 allocates execution resources for an indexer portion of a query. Asdescribed herein, for the indexer search portion of an indexer search,the system 16 can determine the number of execution resources toallocate based on the number of indexers 206 to be used to execute theindexer portion of the query. For example, the system 16 can allocate apredetermined number of execution resources for each indexer 206 for theindexer portion of the query. In some such embodiments, the system 16can determine the query-resource allocation for the indexer portion byaggregating the predetermined number of execution resources allocatedfrom each indexer 206 for the query. In addition, as described herein,for an intermediary search, the system 16 can allocate executionresources for an indexer portion based on the number of indexers 206 tobe used to execute the query and a weighting factor.

In certain embodiments, for an indexer portion of a worker node search,the system 16 can allocate execution resources based on an executionresource allocation policy. In some such embodiments, the system 16 canallocate execution resources based on the lesser of the number ofbuckets to be exported from an indexer 206, the number of availablecores or pipelines of the indexer 206, or a threshold number of cores orpipelines.

For worker node portions of a query, the system 16 can use a variety oftechniques to determine the query-resource allocation. In some cases,the system 16 can assign the same quantity of worker nodes 3306 and/orcompute resources of the worker nodes 3306 for each query. In someembodiments, the system 16 can assign worker nodes 3306 and/or computeresources of the worker nodes 3306 based on the size of the query and/ora priority level. As described herein, in some embodiments the size ofthe query can correspond to a number of records to be processed, thesize of the records to be processed, and/or an amount of memory used tostore the records, etc.

In certain embodiments, the system 16 can determine the query-resourceallocation for a worker node portion based on the processing task thatprocesses and/or generates the largest number of records. In certainembodiments, the system can determine query-resource allocation for eachprocessing task of a worker node portion of the query. As describedherein, the system 16 can allocate more compute resources for largerqueries (or larger processing tasks) and fewer compute resources forsmaller queries (or smaller processing tasks).

Additionally, as described herein, the system 16 can determine aquery-resource allocation based on a priority level, query executiontime threshold, and/or query completion time. In some such embodiments,the system 16 can allocate more compute resources for queries with ahigher priority level, queries that are to be executed within less timeor sooner than for queries with a lower priority level, queries that areto executed in more time or later, etc. In addition, as describedherein, the system can assign a priority level based on the queryexecution time threshold or the query completion time.

In some embodiments, the system 16 can determine a range ofquery-resource allocations for the query and/or the portions of thequery. For example, the system 16 can indicate that a particular numberof execution or compute resources is preferred, but that a differentnumber of execution or compute resources are acceptable to execute thequery or that at least a certain number of execution or computeresources are to be allocated. For example, for an indexer portion ofthe worker node search, the system 16 can indicate that 12 executionresources from each indexer 206 is preferred, but also indicate that thequery can be executed if at least three execution resources from eachindexer 206 can be allocated. Similarly, the system 16 can indicate thatthe query has a first priority level, but that if there are insufficientresources to execute the query at the first priority level, then it canbe executed at a second priority level (with fewer execution or computeresources). In this way, the system 16 can provide flexibility inscheduling the query for execution. At block 6806, the search head 210determines execution or compute resource availability for one or moreportions of the query. In some cases, the search head 210 determines theexecution or compute resource availability for the different portions ofthe query based on the total number of execution or compute resources ofthe components that will be used to execute that portion of the queryand the amount of execution or compute resources of those componentsthat are allocated to other queries. For example, if a first portion ofthe query corresponds to an indexer portion of the query (indexersearch, intermediary search, or worker node search), the search head 210can determine the total amount of execution resources available from theindexers 206 to execute the query based on the total number of executionresources of the indexers 206 and the amount of execution resources ofthe indexers 206 allocated to other queries. Similarly, for a workernode portion of a query, the search head 210 can determine the totalamount of compute resources available by the worker nodes 3306 toexecute the query based on the total number of compute resources of theworker nodes 3306 and the amount of execution resources of the indexers206 allocated to other queries. As another example, for worker nodeportions of the query, the system 16 may indicate a fixed number ofworker node searches are supported. In some such embodiments, for workernode portions of worker node searches, the system 16 can determine thecompute resource availability of the worker nodes 3306 based on thenumber of worker node searches being executed or scheduled for executioncompared to the number of worker node searches that are supported.

It will be understood, that in some embodiments, the search head 210 candetermine an execution or compute resource availability for only oneportion of the query. For example, if the query is an indexer search oran intermediary search and does not use other components of the dataintake and query system 16, such as the worker nodes 3306, the searchhead 210 may determine an execution resource availability for only theindexer portion of the indexer search for intermediary search.

In certain embodiments, the search head 210 can also determine anexecution or compute resource availability for other portions of thequery, such as a results portion of the query or an external data systemportion of the query, etc.

At block 6808, the search head 210 schedules the query. The search head210 can schedule the query based on the determined the execution orcompute resource availability and the query-resource allocation. Forexample, if the search head 210 determines that there are sufficientexecution or compute resources for the different portions of the queryto satisfy the query-resource allocations for those portions, the searchhead 210 can schedule the query for execution at that time. However, ifthe search head 210 determines that there are insufficient execution orcompute resources for the different portions of the query to satisfy thequery-resource allocations, the search head 210 can schedule the queryfor execution at a future time. In some cases, the search head 210places the query in a queue for execution at a future time, and in somecases, determines the time at which the query is to be executed.

In some embodiments, the search head 210 can use the range ofquery-resource allocations to schedule the query. For example, if thesearch head 210 determines that there are insufficient execution orcompute resources to execute the query using a preferred query-resourceallocation, but there are sufficient execution or compute resources toexecute the query using an alternate query-resource allocation, thesearch head 210 can schedule the query for execution using the alternatequery-resource allocation.

In certain embodiments, the search head 210 can use the query executiontime threshold and/or query completion time to schedule the query. Forexample, if there are sufficient resources to execute a query uponreceipt, but the query completion time is later than a query completiontime of a query in a queue, the search head 210 can place the query inthe queue. As another example, if there are sufficient resources toexecute the query within a particular time, but that time does notsatisfy the query execution time threshold, the search head 210 canplace the query in a queue until there are sufficient resourcesavailable to execute the query within the query execution timethreshold.

Furthermore, as described herein, in some embodiments, the search head210 can allocate different amounts of execution or compute resources todifferent portions of the query at different times. For example, duringa worker node portion of the query, the search head 210 can assign adifferent number of execution or compute resources to execute differentprocessing tasks of the worker node portion. As another example, duringexecution, if the search head 210 is unable to provide the preferrednumber of execution or compute resources for the query and additionalresources become available during execution, the search head 210 canassign additional execution or compute resources to the query. In thisway, the search head 210 can dynamically allocate and assign executionor compute resources to execute the query. In some embodiments, thesearch head 210 may not dynamically allocate execution or computeresources during execution of the query. For example, based on aninitial query-resource allocation, the search head 210 can schedule andexecute the query.

It will be understood that fewer, more, or different blocks can be usedas part of the routine 6800. In some cases, one or more blocks can beomitted. For example, block 6802 can be omitted. In certain embodiments,the block 6808 can be replaced with executing the query based on thequery-resource allocation.

Moreover, it will be understood that one or more blocks described hereinwith reference to routine 6800 can be combined with one or more blocksof other routines described herein, such as the routines describedherein at least with reference to FIGS. 5, 6, 23-26, 31, 34, 38-45, 47,49, 52-57, 63, 65, 66, 67, 69, 71, and 73 . In certain embodiments, anyone or any combination of 6802-6810 can be part of a query processingstage, as described herein. Furthermore, it will be understood that thevarious blocks described herein with reference to FIG. 68 can beimplemented in a variety of orders. For example, blocks 6804 and 6806can be implemented concurrently, etc.

39.0. Search Time Estimate

Queries executed by the data intake and query system 16 can varysignificantly in size, the amount of data processed, and the time ittakes to execute the query. In some cases, queries executed by the dataintake and query system 16 can take hours, or days, or longer. Queriesthat take significant amounts of time can reduce the query executionthroughput of the system 16 and/or reduce the amount of queries that canbe executed by the data intake and query system 16. In some cases, whena user enters a query, they do not know how much time the query willtake. Accordingly, if the query being executed is time sensitive, thesystem 16 may be unable to determine whether the query will be finishedby a particular time. Similarly, if the user provides a query completiontime, the system 16 may be unable to determine whether the query can becompleted by the query completion time. This can increase the difficultyof scheduling queries for execution and executing those queries.

Accordingly, in some cases, the system 16 can be improved by estimatingthe query execution time of a query before it is executed. Bydetermining the query execution time, the system 16 can enable a user tomodify the query so that it can be executed in less time. For example,the user may determine that a smaller set of data can be used for thequery, can increase the priority level of the query, and/or terminateother queries.

However, it can be difficult to determine the query execution time for aquery to be executed by the data intake and query system 16. Forexample, as described herein, a query executed by the data intake andquery system 16 can include different portions executed by differentcomponents of the data intake and query system 16. In addition,executing the query can include processing data by one or more indexers206, one or more worker nodes 3306, search heads 210, and/or one or moreexternal data systems 12. The complexity of the system 16 can make itdifficult to determine the query execution time of the query.

To address these and other potential issues, the system 16 can identifydifferent portions of the query or different subqueries, which can beexecuted by different components of the data intake and query system 16.As described herein, at least with reference to FIG. 68 , one portion ofa query or subquery can be executed by one or more indexers 206, asecond portion of the query can be executed by one or more worker nodes3306, a third portion of a query can be executed by an external datasystem 12, and a fourth portion of the query can be executed by a searchhead 210. The system 16 can determine a query execution time for each ofthe portions of the query and use the execution times of the differentportions to determine the query execution time as a whole.

Determining the execution time for each query portion can vary dependingon the amount of data processed during the query portion and thecomponents of the data intake and query system 16 (or external datasystem 12) executing the query portion. In certain embodiments, thesearch head 210 can determine the execution time for a query portion tobe executed by the one or more indexers 206 based on the number ofbuckets, amount of bucket data, and number of execution resourcesallocated to process the bucket data. For example, as described herein,at least with reference to FIG. 63 , the system can allocate bucket datato execution resources for processing and/or export. Based on the bucketdistribution, the system 16 can identify which of the executionresources will take the longest time to process the data. In someembodiments, the system 16 can determine that the query portionexecution time for the query portion executed by the indexers 206corresponds to the execution time of the slowest execution resource.

In certain embodiments, the search head 210 can determine the queryexecution time for a worker node portion of a query based on the amountof data received by the worker nodes 3306, the number of processingtasks executed by the worker nodes 3306, the amount of recordsprocessed/generated by each processing task of the worker nodes 3306,and/or the query-resource allocation of the worker node portion of aquery, etc.

As described herein, at least with reference to FIGS. 66, 67, and 68 , aquery can include multiple processing tasks for execution by the workernodes 3306. Accordingly, in certain cases, the system 16 can identifythe processing tasks for execution by the worker nodes 3306, determinethe number of records processed/generated by the worker nodes 3306,determine a query-resource allocation for the processing task, and basedon the query-resource allocation and the number of records to beprocessed/generated, determine an execution time for a particularprocessing task.

In some embodiments, the search head 210 can estimate the execution timefor a processing task of a worker node portion of a query based on aheuristically-determined data model that indicates an amount of time toprocess a particular number of records using a particular number ofcompute resources. For example, the search head 210 can compare thenumber of records to be processed/generated by a processing task and thenumber of compute resources allocated for the processing task with theheuristically-determined data model to determine the execution time forthe particular processing task of the worker node portion of the query.

In certain embodiments, the system 16 can combine the execution time ofthe processing tasks to determine the query execution time for theworker node portion of a query. In certain embodiments, the system 16can combine the execution time of the different processing tasks bysumming the execution time of the different processing tasks or summingthe processing tasks that are to be executed sequentially.

In some cases, the search head 210 can determine the query executiontime for external data systems in a manner that is similar to the way inwhich subquery execution times are determines for the indexers 206. Forexample, as described herein, in some cases, the external data system 12can be another data intake and query system 16. In some suchembodiments, the search head 210 can use an estimate provided by theexternal data system 12 to determine the query execution time for thesubquery executed by the external data system 12.

In embodiments where the external data system is not another data intakeand query system 16, the search head 210 can cause one or more workernodes 3306 to interact with the external data system 12 to determine aquery execution time. In some embodiments, a query execution time may beprovided in a configuration file or the external data system may be ableto provide a query execution time based on the query that it receivesfrom a worker node 3306.

In some cases, the system 16 can also determine the execution time forportions of the query executed by the query coordinator 3304 and/or thesearch head 210. In some cases, the system can also take into accountcertain time that is required to communicate data between components ofthe data intake and query system 16 (e.g., between indexers 206/workernodes 3306, worker nodes 3306/query coordinator, querycoordinator/search process master/search head 210, etc.).

Once the system 16 determines the query execution time for the variousportions of the query, it can determine the query execution time for theentire query. In certain embodiments, the system 16 determines the queryexecution time for the query by summing the determined query executiontime of each portion of the query. As mentioned, the system 16 can, insome cases, include other time requirement of the query when determiningthe query execution time. For example, the system 16 can include thetime required to communicate data between different components of thesystem 16.

Once determined, the system 16 can provide the query execution time tothe user. In some cases, the system 16 can use the query execution timeto schedule queries for execution, modify priority levels, etc. Forexample, the system 16 can determine that by scheduling two smallerqueries concurrently, it will have more execution resources availablefor a larger query and can therefore process all of the queries in lesstime or in a more efficient manner.

FIG. 69 is a flow diagram illustrative of an embodiment of a routine6900 implemented by a search head 210 to determine a query executiontime for a query. Although certain blocks are described as beingimplemented by a search head 210, it will be understood that theelements outlined for routine 6900 can be implemented by one or morecomputing devices/components (alone or in combination) that areassociated with a data intake and query system 16, such as the workernode 3306, search process master 3302, the query coordinator 3304, etc.Thus, the following illustrative embodiment should not be construed aslimiting. Moreover, it will be understood that routine 6900 is notlimited to a data intake and query system 16, but can be used todetermine query execution time estimates in a variety of systems andenvironments.

At block 6902, the search head 210 receives a query, as describedherein, at least with reference to block 6702 of FIG. 67 , FIG. 6 , andelsewhere. At block 6904, the search head 210 identifies one or morequery portions. In some embodiments, the query portions can correspondto portions of the query or subqueries to be executed by differentportions of the data intake and query system 16, as described herein atleast with reference to FIGS. 6, 41, 42, 52-56, 60, and 68 . Forexample, one query portion can correspond to the portion of the query(or a generated or identified subquery) to be executed by the indexers206 and another query portion can correspond to the portion of the query(or a generated or identified subquery) to be executed by the workernodes 3306, an external data system 12, the search head 210, or querycoordinator 3304, etc. In certain embodiments, the search head 210 candetermine the query portions and the components that are to execute thequery portions based on the syntax and semantics of the query. Incertain embodiments, the search head 210 can determine that indexers 206are to extract the set of data identified by the query, the worker nodes3306 are to process and/or transform the extracted data, and the querycoordinator 3304 and/or search head 210 are to collate and finalize theresults of the query, etc.

At block 6906, the search head 210 determines an execution time for oneor more portions of the query. As described herein, the search head 210can determine an execution time for an indexer portion of the querybased on one or more of the amount of bucket data, the number ofbuckets, the number of allocated execution resources, the type of query,etc.

In addition, the search head 210 can determine an execution time for aworker node portion of the query based on the processing tasks (numberand/or type) to be executed by the worker nodes, the amount of recordsprocessed/generated by the worker nodes 3306, and the amount of computeresources of the worker nodes 3306 allocated to the worker node portionof the query.

As mentioned, in some embodiments, the search head 210 can estimate theexecution time for a particular processing task based on a comparison ofthe number of records and allocation of compute resources with aheuristically-determined data model that indicates an amount of time toprocess a particular number of records using a particular number ofcompute resources.

In addition, the search head 210 can determine an execution time for aresults portion of the query and/or one or more data transport portionsof the query (e.g., time to transport data between different componentsof the system 16.

Further, the search head 210 can determine an execution time for anexternal data system 12. In some cases, the search head 210 candetermine the execution time for the external data system 12 similar tothe way in which the search head 210 determines the execution time forthe indexers 206. For example, if the external data system 12 is anotherdata intake and query system 16, the search head 210 can communicate thesubquery for the other data intake and query system 16 and the otherdata intake and query system 16 can provide an execution time. Incertain cases, the search head 210 can determine an execution time forthe external data system 12 based on a predetermined (or provided)estimate or based on a previously measured execution time. For example,the system 16 can communicate a subquery to the external data system 12and measure the amount of time to receive results from the external datasystem 12.

At block 6908, the search head 210 determines a query execution time forthe query. In some embodiments, the search head 210 can determine thequery execution time for the query based on the query execution time forthe different query portions. In certain embodiments, the search head210 can determine the query execution time by adding the execution timeof the different query portions. In some cases, some parts of the querymay be performed concurrently or in parallel. For example, the indexers206 may execute an indexer portion of the query concurrently with one ormore external data systems 12. Similarly, as worker nodes 3306 receivedata from the indexers 206 and/or external data system 12, they canbegin processing the data concurrently with the indexers 206 and/orexternal data systems 12. The search head 210 can take into account anyconcurrent processing between the different components of the dataintake and query system 16 or external data systems 12 as it determinesthe query execution time for the query.

In certain embodiments, the search head 210 can also use additionalinformation to determine the query execution time. For example,different portions of the query may take a predetermined amount of timeor may not vary significantly between queries, such as, but not limitedto, communicating chunks of data from the indexers 206 to the workernodes 3306, communicating results from the worker nodes 3306 to thequery coordinator 3304, and/or communicating results from the querycoordinator to the search process master or search head 210, etc.Accordingly, the search head 210 can use the estimated timecorresponding to communicating information between components of thedata intake and query system 16 to determine the query execution time.

It will be understood that fewer, more, or different blocks can be usedas part of the routine 3800. In some cases, one or more blocks can beomitted. For example, in certain embodiments, the results received fromnodes 3306 can be in a form that does not require any additionalprocessing by the query coordinator 3304. In some such embodiments, thequery coordinator 3304 can communicate the results without additionalprocessing. As another example, the routine 3800 can include monitoringworker nodes 3306 during execution of the query or query processingscheme, allocating or deallocating resources during the execution of thequery, etc. Based on any reallocations, the system 16 can determine anupdated execution time of the query. Similarly, routine 3800 can includereporting completion of the query to a component, such as the searchprocess master 3302, etc.

Moreover, it will be understood that one or more blocks described hereinwith reference to routine 6900 can be combined with one or more blocksof other routines described herein, such as the routines describedherein at least with reference to FIGS. 5, 6, 23-26, 31, 34, 38-45, 47,49, 52-57, 63, 65-67, 68, 71, and 73 . Furthermore, it will beunderstood that the various blocks described herein with reference toFIG. 69 can be implemented in a variety of orders. For example, blocks6904-6908 can be implemented concurrently, etc.

40.0. Processing High Cardinality Records with Related Fields

The worker nodes 3306 can receive a variety of records from the indexers206. In some cases, the worker nodes 3306 receive relatively largerecords that include multiple sub-records. In certain cases, one recordcan include thousands, millions, or even more sub-records. For example,executing a “stats dc (field A) by field B” command, or other commandthat identifies an association between multiple data fields, on a set ofdata can result in records with hundreds of thousands or moresub-records per record.

Large records can impede the worker nodes 3306 ability to store therecords in partitions and process the partitions. For example, a workernode 3306 may have a limited amount of memory to store partitions and ifthe worker node 3306 receives many large records to store in thepartition, it may run out of memory space, generate a memory error, orbe unable to assign additional records to additional partitions. Thiscan reduce system performance, result in the failure of the query tocomplete and/or result in the loss of data.

To address this and other potential issues, the system 16 can generatemultiple records from a large record, assign the generated records toone or more partitions, and then combine similar records across thepartitions. By breaking a large record into smaller records, the system16 can be improved. For example, the system 16 can reduce the amount ofmemory used by a particular partition, reduce the likelihood of or avoidrunning out of memory for a particular partition, concurrently processthe generated records in less time, and increase the throughput of thesystem 16.

FIG. 70 is a block diagram illustrating an example of an embodiment inwhich individual records from multiple chunks of data are used togenerate multiple records, which are stored in multiple partitions. Theillustrated embodiment further illustrates the combination of similarrecords across multiple partitions and the reduction of those records.

In the illustrated embodiment, three chunks (Chunk 1, Chunk 2, and Chunk3) are to be processed by a worker node 3306. In some cases, Chunks 1,2, 3 can correspond to chunks of data or portions of chunks received bya worker node 3306 from one or more indexers 206 in response to a query.For example, an indexer 206 may send a chunk of 50,000 records to aworker node 3306. The worker node 3306 may break up the 50,000 recordsinto groups or sub-chunks (e.g., each with 50 or 100 records) tofacilitate processing. Accordingly, Chunks 1, 2, and 3 can correspond todifferent chunks of data received from one or more indexers 206 orsub-chunks of the chunks of data received from the one or more indexers206. In some cases, Chunks 1, 2, and 3 correspond to chunks receivedfrom the same indexer 206 or chunks received from different indexers 206(e.g., Chunk 1 from indexer 1, Chunk 2 from indexer 2, and/or Chunk 3from indexer 3).

As described herein, each chunk of data can include many records. In theillustrated embodiment, three records from each chunk are shown. Asdescribed herein, in some embodiments, each record of a chunk cancorrespond to one or more events or portions of event(s) that have beenprocessed or transformed by the indexers 206 based on a query. Forexample, each record may be generated based on or include a portion ofan event stored in a data store 208.

The field values of a record can depend on a field value of acorresponding event and/or a field value generated during queryexecution. For example, some of the field values of each record candepend on the data of the corresponding event (e.g., Field 1 and Field2), while others may depend on data that is generated as events orrecords are processed (e.g., Count Field). In the illustratedembodiment, the field values of Field 1 and Field 2 of each record ofthe Chunks 1, 2, 3, can be based on the data in a corresponding event,and the field value of the Count Field can be based on data generated asevents/records are processed by the system 16.

As described herein, the records of each chunk can be based on thecommands of the query. In the illustrated embodiment, the records ofChunks 1, 2, and 3 can be based on a query that includes a command thatindicates a relationship between two fields, such as, but not limitedto, “stats DC (Field 1) by Field 2.” However, it will be understood thata variety of commands can result in records similar to those shown inthe illustrated embodiment or that otherwise result in large records orrecords with a large number of sub-records. In certain cases, thecommand indicates a relationship between two fields where one or bothfields have high cardinality field values.

In some cases, one or more (or all) of the records of a chunk of datacan include multiple sub-records. In some such embodiments, sub-recordsof a record can share the same field value for some fields and differentfield values for other fields. For example, the sub-records of a recordcan share the same field value for one field, different field values fora second field, and the same or different field values for a thirdfield, etc.

In the illustrated embodiment of FIG. 70 , each of the Chunks 1, 2, 3includes three records. The Records 1, 2, and 3, of Chunks 1 and 3 andRecords 1 and 2 of Chunk 2 each include multiple sub-records. The Record3 of Chunk 2 includes only one record (or one sub-record). The followingtable summarizes the number of sub-records per record in the illustratedembodiment, however, it will be understood that other records can havedifferent numbers of sub-records depending on the query:

No. of Chunk No. Record No. Sub-Records 1 1 3 1 2 2 1 3 5 2 1 8 2 2 8 23 1 3 1 4 3 2 4 3 3 5

In the illustrated embodiment of FIG. 70 , for the records withsub-records, each sub-record of a record shares the same field value forField 1 and has a different field value for Field 2. In addition, eachsub-record in the illustrated embodiment includes a Count Field whichmay or may not match the count for another sub-record of the samerecord. However, it will be understood that other records can havedifferent numbers of fields and/or different combinations of matchingfields, depending on the query

As described herein, each record from a chunk of data is typicallyassigned to a partition as one record. In some embodiments, eachpartition is configured to store approximately the same number ofrecords or use approximately the same amount of memory. In certainembodiments, the worker node 3306 may be able to vary the amount ofrecords per partition or the amount of memory used per partition toaccommodate related records or to complete a task.

In the illustrated embodiment, each of the Partitions 1, 2, 3 isconfigured to hold six records. Accordingly, if the three records fromChunk 1 and Chunk 2 are assigned to Partition 1, Partition 1 would storesix records with a total of twenty-seven sub-records. Alternatively, ifonly records with field value A for Field 1 are assigned to Partition 1,then Partition 1 would store the records 7002 (Record 1 of Chunk 1),7004 (Record 1 of Chunk 2), and 7006 (Record 1 of Chunk 3), totaling 15sub-records, plus potentially three more records from additional chunksof data. Given that Partition 1 is configured to store six records, sucha large amount of data or large number of sub-records compared to thePartition 1's configuration may exceed the storage limits of thepartition and result in some of the records from Chunks 1, 2, or 3 notbeing processed.

To avoid this scenario, the worker node 3306 can generate a record fromeach sub-record of a chunk of data. In the illustrated embodiment ofFIG. 70 , the worker node 3306 has generated a record for eachsub-record of records 7002, 7004, 7006, (sub-records of Chunks 1, 2, 3with field value A for Field 1). Although not illustrated, the workernode 3306 can generate a record for each sub-record with field value Bor C for Field 1. However, for simplicity, only records generated fromrecords with field value A for Field 1 are shown. Accordingly, in theillustrated embodiment, the worker nodes 3306 generates three recordsfrom record 7002, eight records from record 7004, and four records fromthe record 7004.

In some cases, the worker node 3306 can assign the generated records tothe same partition (or group of partitions) based on the shared fieldvalue. In the illustrated embodiment of FIG. 70 , the worker node 3306assigns the 15 generated records with field value A for Field 1 to oneof Partitions 1, 2, and 3.

In some cases, the worker nodes 3306 can assign the generated records toone of the group of partitions based on the time of assignment and/orthe content of the partitions. For example, as one partition is filledwith records, another partition can be assigned to accept additionalrecords. In the illustrated embodiment, each of the Partitions 1, 2, 3can hold up to six records. Accordingly, as shown at 7008, the workernode 3306 assigns the three records generated from the record 7002 andthe first three records of the eight records generated from the record7004 to Partition 1. With Partition 1 filled to capacity, the workernode 3306 assigns the remaining five records generated from the record7004 to Partition 2. Finally, the worker node 3306 assigns the firstrecord of the four records generated from the record 7006 to Partition 2(meeting its capacity) and the last three records generated from therecord 7006 to Partition 3.

As illustrated above, the sub-records from different chunks of data canbe assigned to the same partition and sub-records from the same chunk ofdata can be assigned to different partitions. For example, as mentioned,Partition 1 includes records generated from sub-records of Chunks 1 and2 and Partition 2 includes records generated from sub-records of Chunks2 and 3. Similarly, records generated from record 7002 are assigned toPartition 1, records generated from record 7004 are assigned toPartitions 1 and 2, and records generated from record 7006 are assignedto Partitions 2 and 3.

In some cases, given the mixing of records from different chunks of datato the same partition, the worker node 3306 can parse the differentpartitions and combine similar records. For example, the worker node3306 can combine records with the same event field values (field valuesthat correspond to the field values of the event(s) related to therecord). In some embodiments, the worker node 3306 can combine recordsby aggregating one or more field values of similar records (e.g.,aggregating a count field value or other generated field value ofrecords with the same event field values). By combining similar records,the worker node 3306 can reduce the amount of memory used by eachpartition and reduce the amount of processing (and therefore executiontime) of later stages, or in some cases eliminate one or more processingstages.

As shown with reference to 7008 and 7010, in the illustrated embodimentof FIG. 70 , the worker node 3306 combines similar records in Partition1, similar records in Partition 2, and similar records in Partition 3.As all of the generated records share the at least the same field valuefor one field (value ‘A’ for Field 1), similar records in this instancecan refer to records that share the same field value for at least twofields (e.g., the value ‘A’ for Field 1 and the same field value forField 2). For example, Partition 1 includes multiple records with thefollowing field values for Fields 1 and 2: A:0 (Records 1 and 4) and A:1(Records 2 and 5). Thus, worker node 3306 combines the A:0 records byaggregating the Count Field for all A:0. Similarly, the worker node 3306combines the A:1 records of Partition 1 and the A:3 records of Partition2 (Records 1 and 6). As Partition 3 does not include any similarrecords, the worker node 3306 does not combine any of Partition 3'srecords.

As the worker node 3306 may not be able to assign all generated recordsto the same partition, some similar records may be found across thegroup of partitions. For example, with reference to the illustratedembodiment, Partitions 1, 2, 3 each include an A:6 record (Records 3, 4,and 2, respectively). Accordingly, the worker node 3306 can reassignrecords to different partitions so that all similar records are found inthe same partition.

The worker node 3306 can reassign records to different partitions in avariety of ways. In some embodiments, the worker node 3306 can use amodulo operand or hash code to reassign records. For example, the workernode 3306 can apply a modulo operand to one or more of the field valuesof the records. If the field values are the same, the records can beassigned to the same partition.

In some cases, the worker node 3306 uses a field value that is differentfrom the already determined matching field value(s) of the sub-records.For example, with reference to the illustrated embodiment of FIG. 70 ,the worker node 3306 can use the Field 2 field value of each record toreassign the records given that it is already determined that therecords of Partitions 1, 2, 3 have the same field value for Field 1.

In certain embodiments, the worker node 3306 can use a generated fieldvalue to reassign the records (e.g., a field value generated during theprocessing of the events or records). For example, the worker node 3306can assign similar records to the partition with the highest (lowest, orsome other amount) count for the similar record. In the illustratedembodiment of FIG. 70 , the worker node 3306 has reassigned the recordsbased on the Count Field value. Specifically, with reference to 7010,the worker node 3306 determines that between Partitions 1, 2, and 3there are multiple A:4, A:6, and A:7 records. In addition, the workernode 3306 determines that Partition 2 has the highest count of A:6records and Partition 3 has the highest count of A:4 and A:7 records.Accordingly, the worker node 3306 assigns the A:4 and A:7 records fromPartition 2 to Partition 3 and the A:6 records from Partitions 1 and 3to Partition 2, as shown at 7012.

In some cases, following the reassignment of records to the differentpartitions, each partition can include a set of records that does notoverlap with the set of records of the other partitions. For example,the combination of Field 1 field values and Field 2 field values in onepartition may not be found in another partition. With reference to 7012,Partition 1 includes all A:0 (Record 1), A:1 (Record 2), and A:2 (Record3) records, Partition 2 includes all A:3 (Record 1), A:5 (Record 2), andA:6 (Records 3-5) records, and Partition 3 includes all A:4 (Records 1and 3) and A:7 (Record 2 and 4) records. In certain embodiments, therecords can be reassigned such that the partitions include the recordsin a particular order (e.g., Partition 1 can include the A:0-A:2records, Partition 2 can include the A:3-A:5 records, and Partition 3can include A:6 and A:7 records) as desired.

Based on the reassignment of records, the worker node 3306 can (again)combine similar records similar records within each partition. Withreference to 7012 and 7014, the worker node 3306 combines the three A:6records of Partition 2, the two A:4 records of Partition 3, and the twoA:7 records of Partition 3 by aggregating the Count Field value of thesimilar records.

The worker node 3306 can continue to process the partitions based on thequery. In the illustrated embodiment of FIG. 70 , based on the query,the worker node 3306 determines a count for the number of distinctrecords with the same Field 1 field value that remain in the partitionsat 7014 and/or a count of the number of unique combinations of the Field1 field value and the Field 2 field value. The results of the processingare shown at 7016. Specifically, each of Partitions 1, 2, 3 has beenreduced to a single record for each Field 1 field value that indicatesthe number of unique Field 2 field values for the Field 1 field value.

It will be understood that the example embodiment shown in FIG. 70 is asimplified example. For example, for simplicity, the example shown inFIG. 70 may only illustrate a subset of the number of records in a chunkof data (e.g., each chunk of data may include thousands, millions, ormore records), a subset of the number of sub-records of a record of achunk of data (e.g., each record may include thousands, millions, ormore sub-records), a subset of the number of records generated from arecord of a chunk of data (e.g., thousands, millions, or more recordscan be generated from a single record), a subset of the number ofrecords per partition (e.g., each partition may include thousands,millions, or more records), a subset of the number of partitions (e.g.,a worker node 3306 may process hundreds, thousands, or millions ofpartitions, etc.). Furthermore, for simplicity, the example shown inFIG. 70 does now show that multiple field values for Field 1 can beassigned to the same partition (e.g., Partition 1 can include recordswith a field value of B for Field 1) or that multiple cores in a workernode 3306 can be working concurrently to process Partitions 1, 2, 3 orthat each core of the worker node 3306 can be processing its own set ofpartitions. Furthermore, the example of FIG. 70 does not show thatmultiple worker nodes 3306 can be working concurrently to process chunksof data received by the indexers 206. Accordingly, the example shown inFIG. 70 should not be construed as limiting.

In some embodiments, the expansion of records, assignment to differentpartitions, and the combination of similar records can be performed bythe worker node 3306 based on one or more factors. In some cases, workernode 3306 can perform these processes based on a query parameteridentified or referenced by the query. For example, if the queryincludes a command “stats DC (Field 1) by Field 2,” or other commandthat identifies an association between two fields, the worker node 3306can perform one or more of these processes. As yet another example, theworker node 3306 can perform these processes based on an identificationof a particular index, host, source, or sourcetype. In certain cases,the worker node 3306 can perform these processes based on a determinedsize of the records of the chunks it receives. For example, if theworker node 3306 determines that each record uses up a threshold size ofmemory or includes a threshold number of sub-records, the worker node3306 can determine that it is to generate multiple records from onerecord and process the generated records as described herein.

FIG. 71 is a flow diagram illustrative of an embodiment of a routine7100 implemented by a worker node 3306 to expand and reduce records fromone or more chunks of data. Although certain blocks are described asbeing implemented by a worker node 3306, it will be understood that theelements outlined for routine 7100 can be implemented by one or morecomputing devices/components (alone or in combination) that areassociated with a data intake and query system 16, such as an indexer206, search head 210, search process master 3302, query coordinator3304, etc. Thus, the following illustrative embodiment should not beconstrued as limiting. Moreover, it will be understood that routine 7100is not limited to a data intake and query system 16, but can be used toprocess high cardinality records in a variety of systems andenvironments.

At block 7102, the worker node 3306 obtains a chunk of data. In someembodiments, the worker node 3306 can obtain a chunk of data asdescribed herein at least with reference to block 6502 of FIG. 65 . Asdescribed herein the chunk of data can correspond to a chunk of datareceived from an indexer 206 or a chunk (or sub-chunk) of data generatedfrom a chunk of data received from the indexer 206. In addition, therecords in the chunk of data can be based on the query and the set ofdata identified by the query.

As described herein, in some cases, based on the query (non-limitingexample: a “stats DC by” command or other command that identifies arelationship between two fields and/or relevant records have highcardinality field values for one or both fields) and the set of dataidentified by the query, one or more records of the chunk of data caninclude multiple sub-records. In some cases, a record can includethousands or millions of sub-records. In certain cases, each sub-recordof a record can share the same field value for at least one field. Thesub-records may or may not share field values of other fields. Forexample, the record can be identified as a field value “A” recordindicating that all sub-records have the field value “A” for the samefield (but may have different field values for other fields). Further,in some cases, the shared field value can correspond to a field value ofone or more events stored in a data store 208 of the system 16. Incertain embodiments, each record or each sub-record can correspond toone event stored in a data store 208. In some cases, each record or eachsub-record can correspond to multiple events stored in a data store 208.

At block 7104, the worker node 3306 generates a plurality of recordsfrom a record of the chunk of data. In some cases, the worker node 3306generates a record for one or more sub-records of the record (or eachsub-record) of the chunk of data. In certain embodiments, similar to thesub-records, each generated record can share the same field value for atleast one field (the same field value that is shared by sub-records ofthe same record). Other field values of generated records may or may notbe the same. In some cases, the worker node 3306 generates a record foreach sub-record of a record from the chunk of data. In some suchembodiments, the number of records generated from a record cancorrespond to the number of sub-records of the record. For example, if arecord has 100,000 sub-records, the worker node 3306 can generate100,000 records from the record.

At block 7106, the worker node 3306 assigns the generated records to oneor more partitions. In some embodiments, the worker nodes 3306 assigns agenerated record to a partition as it is generated. In some suchembodiments, the assignment may be based on the time of the assignmentor based on a first generated first assigned type assignment. In certainembodiments, the worker node 3306 assigns generated records (recordsgenerated from the same record) to the same partition until thepartition is filled. Once the partition is filled, the worker nodes 3306can assign generated records to a subsequent partition. In certainembodiments, each partition can be allocated up to approximately thesame number of records or use up to approximately the same amount ofmemory.

In some cases, the worker nodes 3306 assigns the generated records basedon a hash code or modulus of one of the fields of the generated recordsor sub-records. For example, in some cases, the worker nodes 3306 canassign the generated records to a partition based on a modulus or hashof a field value of the field that is different from the field with theshared field value. In some such cases, this type of assignment may besimilar to the assignment of records described herein at least withreference to FIG. 65 . In some embodiments, by assigning the generatedrecords based on hash code or modulus, the worker nodes 3306 canfacilitate the combination of similar records.

As described herein, based on the assignment of generated records topartitions, one partition can receive records from multiple chunks ofdata and/or records generated from the same chunk of data can beassigned to different partitions. Accordingly, in some embodiments,similar records can be assigned to the same partition. In some suchembodiments, the similar records may correspond to sub-records ofrecords from different chunks of data. As described herein, in somecases, similar records can correspond to records that share the samefield value for one or more fields. In certain embodiments, as all ofthe generated records may share the same field value for one field, thesimilar records in this case may correspond to records that share thesame field value for at least two fields.

At block 7108, the worker node 3306 combines records of a partition ofthe one or more partitions. As described herein, the worker node 3306can combine similar records, such as records with the same field valuefor at least two fields. The two fields may correspond to event fieldsor fields that are based on content of one or more events stored in adata store 208 as opposed to field values generated during queryexecution (e.g., a count field value). In some embodiments, the workernode 3306 can combine similar records by aggregating one or more fieldvalues of the records. For example, if the records include a countfield, the worker node 3306 can aggregate the count field values.Further, in combining the records, the worker node 3306 can reduce thesimilar records to a single record. For example, three similar recordscan be reduced to one record.

At block 7110, the worker node 3306 combines records across the one ormore partitions. In some cases, to combine records across the one ormore partitions, the worker node 3306 can reassign records. In certainembodiments, the worker node 3306 reassigns records such that similarrecords are assigned to the same partition. For example, the worker node3306 can use a hash function or modulo on the field value of aparticular field of each record in the partitions. In certain cases, theparticular field is different from the field with the same field valueacross all records generated from the same record of a chunk of data.Based on the results of the hash function or modulo, the worker node3306 can reassign the record to a particular partition. In this way,records with the same field value for the particular field can beassigned to the same partition. In certain embodiments, the worker node3306 can identify the partition with the highest count for a particularsimilar record and assign all others records that are similar to theparticular similar record to that partition.

In addition, based on the reassignment, the worker node 3306 can combinerecords of the partitions. In some cases this combination of records ofthe partition can be similar to the combination of records describedherein at least with reference to block 7108.

At block 7112, the worker node 3306 processes the one or morepartitions. In some embodiments, the worker node 3306 can continue toprocess the partitions based on the query. In some embodiments, thecommand that led to the initiation of routine 7100 can includeadditional processing tasks. For example, as described herein, in somecases, the worker node 3306 can combine all records with the same fieldvalue of the first field in a partition. In some such embodiments, tocombine the records with the same field value in the partition, theworker node 3306 can count the number of records with the same fieldvalue of the first field and generated a record that identifies thefield value of the first field and the count of the remaining recordsthat included the same field value of the first field. In some suchembodiments, the resulting partition can include one record for eachunique field value of the first field and a count corresponding to thenumber of unique combinations of the first field value of the firstfield and field values of a second field.

It will be understood that fewer, more, or different blocks can be usedas part of the routine 7100. In some cases, one or more blocks can beomitted. For example, block 7108 can be omitted. In some suchembodiments, the worker node 3306 may reassign records before attemptingto combine similar records. As another example, in some cases, blocks7106 and 7108 can be combined. For example, as the worker nodes 3306assigns records to one or more partitions, the received records can becombined with similar records that are already assigned to thepartition. In some embodiments, the combination blocks 7106 and 7108 canbe similar to the combination described herein at least with referenceto FIG. 65 .

Moreover, it will be understood that one or more blocks described hereinwith reference to routine 7100 can be combined with one or more blocksof other routines described herein, such as the routines describedherein at least with reference to FIGS. 5, 6, 23-26, 31, 34, 38-45, 47,49, 52-57, 63, 65-69, and 73 . In certain embodiments, any one or anycombination of 7102-7110 can be part of a query execution stage, asdescribed herein. Furthermore, it will be understood that the variousblocks described herein with reference to FIG. 71 can be implemented ina variety of orders. For example, blocks 7104-7108 can be implementedconcurrently, etc.

41.0. Pushing Processing Tasks

Queries executed by the system 16 can create different demands ondifferent components of the system 16. For example, based on the queryparameters and syntax, certain processing tasks may be performed ondifferent components of the system 16. For example, some queries may useminimal indexers 206 but use a significant number of worker nodes 3306to execute a query (e.g., queries that include multiple commands thatexpand the number of records). As another example, one query may resultin minimal processing by a query coordinator 3304 or search head 210,while another query may result in significant processing being done bythe query coordinator 3304 or search head 210.

In addition, depending on the query, some components may be able toconcurrently execute commands or processing tasks of the query, whileother components may execute the command or processing task by itself orsequentially. Executing a command of a query using one component asopposed to multiple components concurrently can negatively impact thesystem 16. For example, the component can create a bottleneck for queryexecution, increase the query execution time, and reduce the overallthroughput of the system 16. In addition, using a single component toexecute a command can increase the likelihood of a memory error orresult in the systems 16's inability to process some of the data.

To address this and other potential issues, the system 16 can, in somecases, assign processing tasks (or parts of a processing task) thatwould be executed by one component to other components, or assign asupplemental processing task to the other components. For example, ifbased on a query, a search head 210 is to perform a particularprocessing task, the system 16 can assign that processing task to theone or more worker nodes 3306, assign a portion of the processing taskto the one or more worker nodes 3306, and/or assign a supplementalprocessing task to the one or more worker nodes 3306. In some cases,this may be referred to as pushing or pushing down a processing task. Incertain embodiments pushing or pushing down a processing task can referto the reassignment, partial reassignment, or assignment of asupplemental processing task from one component to another group ofcomponents that execute processing tasks prior to the one component. Assuch, pushing or pushing down a processing task can refer to moving orexecuting processing tasks earlier in a pipeline or DAG.

By pushing a processing task, the system 16 can reduce the strain on acomponent or reduce the likelihood that the component will create abottleneck. In addition, the system 16 can more evenly distribute theprocessing tasks to the components of the system 16, thereby increasingthe parallelized execution of the query, increasing the query executionthroughput, and decreasing the query execution time. As such pushingprocessing tasks can improve the functioning of the system 16 itself, aswell as improve the functioning of distributed systems.

As a non-limiting example, based on the query, the system 16 maydetermine that the search head 210 is to analyze all of the records ofmultiple partitions to identify a particular subset of the records foradditional processing or as query results. In some such cases, analyzingthe records of the partitions may result in the search head 210analyzing millions or billions of results do identify a relatively smallsubset of the records for further processing. For example, it may bethat the search head 210 is to analyze 50,000,000 records across 100partitions to identify the top 10,000 records. Analyzing significantlymore records than will be used for further processing can take asignificant amount of time and create a bottle neck at the search head210.

To reduce the bottleneck, the system 16 can push the command ofidentifying the top 10,000 records to the worker nodes 3306 to performon each of the 100 partitions. As such, rather than the worker nodes3306 sending all of the records from each partition to the search head210, the worker nodes 3306 can send at most 10,000 records from each ofthe partitions. As such, the search head 210 can analyze 1,000,000records to identify the top 10,000 records (rather than analyzing50,000,000 records). Accordingly, the system 16 can significantly reducethe amount of processing to be performed by the search head 210. Inaddition, using the worker nodes 3306, the system 16 can parallelizesome of the processing that was to be done by the search head 210,thereby reducing the execution time of the processing task and the queryexecution time.

FIG. 72 is a block diagram illustrating an example of an embodiment ofthe system 16 assigning a processing task to one or more worker nodes3306 from a search head 210 and/or a query coordinator 3304. Asdescribed herein, pushing the processing task to one or more workernodes 3306 can refer to reassigning the processing task, assigning aportion of the processing task, or assigning a supplemental processingtask to the one or more worker nodes 3306. In some embodiments, bypushing the processing task, the system 16 can reduce the amount ofprocessing performed by the search head 210, remove or reduce potentialbottleneck at the search head 210, increase the parallelized executionof the query, and reduce the query execution time.

In some embodiments, the system 16 can determine to push a processingtask based on one or more query parameters. For example, the system 16can identify a particular command of a query that can be pushed to othercomponents (e.g., head, tail, etc.). In certain embodiments, the system16 can determine to push a processing task based on a sequence ofcommands or the syntax of the query. For example, if a particularsequence of commands is included in the query (e.g., sort . . . | . . .head/tail . . . ), then the system 16 can determine that a command canbe pushed. In some cases, the system 16 can determine whether to push aprocessing task based on a field identified in the query. For example,for some fields or field-command combinations (e.g., host, source,sourcetype), the system 16 may be able to push a command but for otherfields or command-field combination (e.g., “sort count”), the system 16may be unable to push a command.

In the illustrated embodiment, based on a query, the worker nodes 3306generate Partitions 1, 2, 3, 4. In some cases, the Partitions 1, 2, 3, 4can correspond to the results or partial results that the worker nodes3306 are ready to communicate to the search head 210 and/or querycoordinator 3304 after performing one or more processing tasks on therecords (or earlier versions of the records). It will be understood thatthe worker nodes 3306 can generate fewer or more partitions depending onthe query.

As shown, each of the Partitions 1, 2, 3, 4 includes a number ofrecords. Specifically, Partition 1 includes eight records, Partition 2includes six records, Partition 3 includes nine records and Partition 4includes five records. It will be understood that the Partitions 1, 2,3, 4 can include fewer or more records. As described herein, in someembodiments, a partition can include thousands, millions, or morerecords.

In the illustrated embodiment, each record of the partitions includes akeyword value for a keyword field and a count value for a count field.In addition, the records within and across the partitions are sorted bythe keyword value. It will be understood that the records of eachpartition may include any number of fields and/or field values and be inany order. Moreover, it will be understood that the records between thePartitions 1-4 may not be sorted as shown.

In the illustrated embodiment, the query includes a command (e.g.,“|head 4”) indicating that following one or more processing tasks, thesearch head 210 is to provide the top four results as a final result.For example, the system 16 may initially determine that the search head210 is to analyze all of the record of Partitions 1, 2, 3, 4. However,rather than having the search head 210 analyze all 28 records of thefour partitions, the system 16 can push the command to the worker nodes3306 such that only the top four results from each partition are sent tothe search head 210 for further processing. In the illustratedembodiment, the worker nodes 3306 send the records with the followingfield keyword and count values to the search head 210, as intermediateresults, as shown at 7202.

Partition Keyword Value:Count 1 C:5, D:7, G:8, H:9 2 J:6, K:7, L:5, N:83 O:7, R:8, T:6, U:9 4 X:4, Y:3, AA:5, BB:3

Accordingly, the system 16 has reduced the number of records to beanalyzed by the search head 210 almost by half (from 28 to 16). As such,the search heard 210 is able to more quickly identify the top fourresults from the received records (G:8, H:9, N:8, U:9) and provide themas a final result, as shown at 7204. Although described has providingthe top results, it will be understood that other commands can be pushedto different components of the system 16. In addition, it will beunderstood that the pushing of commands can be implemented in a varietyof distributed systems where a particular processing task is assigned toone component and where the system assigns a portion of the processingtask or a supplemental processing task to a group of other componentssuch that the processing load of the initially assigned component isreduced.

Furthermore, in some cases, the system 16 can push the command to theworker nodes 3306 so that the worker nodes 3306 provide the finalresults to the search head 210. For example, rather than sending theintermediate results 7202 to the search head 210, the worker nodes 3306can assign the intermediate results to one or more partitions andperform the same process on the partition(s) that hold the intermediateresults 7202. The worker nodes 3306 can iteratively process the recordsuntil the final results 7204 are determined. The worker nodes 3306 canthen provide the final results to the search head 210.

FIG. 73 is a flow diagram illustrative of an embodiment of a routine7300 implemented by the system 16 to push a processing task from onecomponent to one or more different components. As described herein,pushing the processing task can refer to reassigning the processingtask, assigning a portion of the processing task, or assigning asupplemental processing task to one or more components different fromthe component that would otherwise execute the processing task. Althoughcertain blocks are described as being implemented by the system 16, itwill be understood that the elements outlined for routine 7300 can beimplemented by one or more computing devices/components (alone or incombination) that are associated with a data intake and query system 16,such as an indexer 206, search head 210, search process master 3302,query coordinator 3304, worker nodes 3306, etc. Thus, the followingillustrative embodiment should not be construed as limiting. Moreover,it will be understood that routine 7300 is not limited to a data intakeand query system 16, but can be used to push processing tasks in avariety of systems and environments.

At block 7302, the system 16 obtains one or more partitions. In someembodiments, one or more worker nodes 3306 obtain the partitions basedon one or more processing tasks executed by the worker nodes 3306 on aplurality of partitions. As described herein, the worker nodes 3306 canreceive chunks of data from the indexers 206 and store records from thechunks of data into one or more partitions. As described herein, therecord of the partitions can include one or more field values for one ormore fields. Some of the fields can correspond to fields of eventsstored in a data store 208 and other fields can correspond to fieldsgenerated based on the query (e.g., count field, etc.)

The worker nodes 3306 can then process the partitions (including theirrecords) based on a query. In some cases, processing the partitionsincludes executing one or more processing tasks on the records of thedifferent partitions. In certain cases, the worker nodes 3306 providethe results of the processing tasks to a search head 210. Accordingly,in some embodiments, the partitions can correspond to the partitionsthat the worker nodes 3306 have processed. In some such embodiments, theworker nodes 3306 may be ready to communicate the records of the one ormore partitions to the search head 210.

As mentioned, the partitions can be generated or obtained based on oneor more query parameters, including one or more commands, the syntax ofthe query, the set of data identified by the query, etc. In addition,the query can identify a processing task that is to be executed by onecomponent of the system 16. In certain cases, the processing task can beto provide a particular quantity of records as a result of the query.Further, the processing task can be designated for execution by onecomponent of the system 16. Based on the identification of theprocessing task and the query (e.g., a sequence of processing tasks,etc.), the system 16 can determine that the processing task is to bepushed to a different set of components. For example, the system 16 candetermine that the processing task is to be pushed to the worker nodes3306 from a search head 210. Accordingly, the system 16 can assign theworker nodes 3306 to execute the processing task, a portion of theprocessing task or a processing task that supplements the processingtask of the other component.

At block 7304, the worker nodes 3306 obtain one or more records fromeach of the one or more partitions. In some embodiments, the workernodes 3306 obtain the one or more records based on the assignmentdetermined by the system 16. In some cases, the worker nodes 3306 obtaina particular quantity of records from each partition based on the query.In certain embodiments, the worker nodes 3306 obtain the records fromthe partitions based on the query. For example, if the query indicatesthat 100 results are to be obtained as a final result, the worker nodes3306 can obtain 100 results from each of the partitions. In some cases,the worker nodes 3306 provide the results from each partition to thesearch head 210 for further processing. In certain embodiments, theworker nodes 3306 provide the results to another partition. In some suchembodiments, the aggregated records from each of the partitions can bereferred to as a set of records.

At block 7306, the system 16 obtains records from a set of records. Asmentioned, the set of records can correspond to records obtained fromeach partition. In some such embodiments, as described herein, thesystem 16 can obtain the same quantity of records from each partition.In certain embodiments, the records obtained from the set of records canbased on the query. For example, if the query indicates that 100 resultsare to be obtained as a final result, the search head 210 (or workernodes 3306) can obtain 100 results from the set of results. Asmentioned, in some cases the set of records can reside in one or morepartitions associated with the worker nodes 3306 or with the search head210. Accordingly, in some cases, the worker nodes 3306 can obtain therecords from the set of records. In certain cases, the search head 210can obtain the records from the set of records.

At block 7308, the system 16 displays query results. In some case, theresults can correspond to the records obtained from the set of records.In certain embodiments, a search head 210 can further process therecords obtained from the set of records to determine the query results.In certain embodiments, the results can be based on the query.

It will be understood that fewer, more, or different blocks can be usedas part of the routine 7300. In some cases, one or more blocks can beomitted. Moreover, it will be understood that one or more blocksdescribed herein with reference to routine 7300 can be combined with oneor more blocks of other routines described herein, such as the routinesdescribed herein at least with reference to FIGS. 5, 6, 23-26, 31, 34,38-45, 47, 49, 52-57, 63, 65-69, and 71 . In certain embodiments, anyone or any combination of 7302-7310 can be part of a query executionstage, as described herein. Furthermore, it will be understood that thevarious blocks described herein with reference to FIG. 7300 can beimplemented in a variety of orders. For example, blocks 7304 and 7306can be implemented concurrently, etc.

42.0. Hardware and Isolated Execution Environment Embodiment

FIG. 74 is a block diagram illustrating a high-level example of ahardware architecture of a computing system in which an embodiment maybe implemented. For example, the hardware architecture of a computingsystem 72 can be used to implement any one or more of the functionalcomponents described herein (e.g., indexer, data intake and querysystem, search head, data store, server computer system, edge device,etc.). In some embodiments, one or multiple instances of the computingsystem 72 can be used to implement the techniques described herein,where multiple such instances can be coupled to each other via one ormore networks.

The illustrated computing system 72 includes one or more processingdevices 74, one or more memory devices 76, one or more communicationdevices 78, one or more input/output (I/O) devices 80, and one or moremass storage devices 82, all coupled to each other through aninterconnect 84. The interconnect 84 may be or include one or moreconductive traces, buses, point-to-point connections, controllers,adapters, and/or other conventional connection devices. Each of theprocessing devices 74 controls, at least in part, the overall operationof the processing of the computing system 72 and can be or include, forexample, one or more general-purpose programmable microprocessors,digital signal processors (DSPs), mobile application processors,microcontrollers, application-specific integrated circuits (ASICs),programmable gate arrays (PGAs), or the like, or a combination of suchdevices.

Each of the memory devices 76 can be or include one or more physicalstorage devices, which may be in the form of random access memory (RAM),read-only memory (ROM) (which may be erasable and programmable), flashmemory, miniature hard disk drive, or other suitable type of storagedevice, or a combination of such devices. Each mass storage device 82can be or include one or more hard drives, digital versatile disks(DVDs), flash memories, or the like. Each memory device 76 and/or massstorage device 82 can store (individually or collectively) data andinstructions that configure the processing device(s) 74 to executeoperations to implement the techniques described above.

Each communication device 78 may be or include, for example, an Ethernetadapter, cable modem, Wi-Fi adapter, cellular transceiver, basebandprocessor, Bluetooth or Bluetooth Low Energy (BLE) transceiver, or thelike, or a combination thereof. Depending on the specific nature andpurpose of the processing devices 74, each I/O device 80 can be orinclude a device such as a display (which may be a touch screendisplay), audio speaker, keyboard, mouse or other pointing device,microphone, camera, etc. Note, however, that such I/O devices 80 may beunnecessary if the processing device 74 is embodied solely as a servercomputer.

In the case of a client device (e.g., edge device), the communicationdevices(s) 78 can be or include, for example, a cellulartelecommunications transceiver (e.g., 3G, LTE/4G, 5G), Wi-Fitransceiver, baseband processor, Bluetooth or BLE transceiver, or thelike, or a combination thereof. In the case of a server, thecommunication device(s) 78 can be or include, for example, any of theaforementioned types of communication devices, a wired Ethernet adapter,cable modem, DSL modem, or the like, or a combination of such devices.

A software program or algorithm, when referred to as “implemented in acomputer-readable storage medium,” includes computer-readableinstructions stored in a memory device (e.g., memory device(s) 76). Aprocessor (e.g., processing device(s) 74) is “configured to execute asoftware program” when at least one value associated with the softwareprogram is stored in a register that is readable by the processor. Insome embodiments, routines executed to implement the disclosedtechniques may be implemented as part of OS software (e.g., MICROSOFTWINDOWS® and LINUX®) or a specific software application, algorithmcomponent, program, object, module, or sequence of instructions referredto as “computer programs.”

43.0. Terminology

Computer programs typically comprise one or more instructions set atvarious times in various memory devices of a computing device, which,when read and executed by at least one processor (e.g., processingdevice(s) 74), will cause a computing device to execute functionsinvolving the disclosed techniques. In some embodiments, a carriercontaining the aforementioned computer program product is provided. Thecarrier is one of an electronic signal, an optical signal, a radiosignal, or a non-transitory computer-readable storage medium (e.g., thememory device(s) 76).

Any or all of the features and functions described above can be combinedwith each other, except to the extent it may be otherwise stated aboveor to the extent that any such embodiments may be incompatible by virtueof their function or structure, as will be apparent to persons ofordinary skill in the art. Unless contrary to physical possibility, itis envisioned that (i) the methods/steps described herein may beperformed in any sequence and/or in any combination, and (ii) thecomponents of respective embodiments may be combined in any manner.

Although the subject matter has been described in language specific tostructural features and/or acts, it is to be understood that the subjectmatter defined in the appended claims is not necessarily limited to thespecific features or acts described above. Rather, the specific featuresand acts described above are disclosed as examples of implementing theclaims, and other equivalent features and acts are intended to be withinthe scope of the claims.

Conditional language, such as, among others, “can,” “could,” “might,” or“may,” unless specifically stated otherwise, or otherwise understoodwithin the context as used, is generally intended to convey that certainembodiments include, while other embodiments do not include, certainfeatures, elements and/or steps. Thus, such conditional language is notgenerally intended to imply that features, elements and/or steps are inany way required for one or more embodiments or that one or moreembodiments necessarily include logic for deciding, with or without userinput or prompting, whether these features, elements and/or steps areincluded or are to be performed in any particular embodiment.

Unless the context clearly requires otherwise, throughout thedescription and the claims, the words “comprise,” “comprising,” and thelike are to be construed in an inclusive sense, as opposed to anexclusive or exhaustive sense, e.g., in the sense of “including, but notlimited to.” As used herein, the terms “connected,” “coupled,” or anyvariant thereof means any connection or coupling, either direct orindirect, between two or more elements; the coupling or connectionbetween the elements can be physical, logical, or a combination thereof.Additionally, the words “herein,” “above,” “below,” and words of similarimport, when used in this application, refer to this application as awhole and not to any particular portions of this application. Where thecontext permits, words using the singular or plural number may alsoinclude the plural or singular number respectively. The word “or” inreference to a list of two or more items, covers all of the followinginterpretations of the word: any one of the items in the list, all ofthe items in the list, and any combination of the items in the list.Likewise the term “and/or” in reference to a list of two or more items,covers all of the following interpretations of the word: any one of theitems in the list, all of the items in the list, and any combination ofthe items in the list.

Conjunctive language such as the phrase “at least one of X, Y and Z,”unless specifically stated otherwise, is otherwise understood with thecontext as used in general to convey that an item, term, etc. may beeither X, Y or Z, or any combination thereof. Thus, such conjunctivelanguage is not generally intended to imply that certain embodimentsrequire at least one of X, at least one of Y and at least one of Z toeach be present. Further, use of the phrase “at least one of X, Y or Z”as used in general is to convey that an item, term, etc. may be eitherX, Y or Z, or any combination thereof.

In some embodiments, certain operations, acts, events, or functions ofany of the algorithms described herein can be performed in a differentsequence, can be added, merged, or left out altogether (e.g., not allare necessary for the practice of the algorithms). In certainembodiments, operations, acts, functions, or events can be performedconcurrently, e.g., through multi-threaded processing, interruptprocessing, or multiple processors or processor cores or on otherparallel architectures, rather than sequentially.

Systems and modules described herein may comprise software, firmware,hardware, or any combination(s) of software, firmware, or hardwaresuitable for the purposes described. Software and other modules mayreside and execute on servers, workstations, personal computers,computerized tablets, PDAs, and other computing devices suitable for thepurposes described herein. Software and other modules may be accessiblevia local computer memory, via a network, via a browser, or via othermeans suitable for the purposes described herein. Data structuresdescribed herein may comprise computer files, variables, programmingarrays, programming structures, or any electronic information storageschemes or methods, or any combinations thereof, suitable for thepurposes described herein. User interface elements described herein maycomprise elements from graphical user interfaces, interactive voiceresponse, command line interfaces, and other suitable interfaces.

Further, processing of the various components of the illustrated systemscan be distributed across multiple machines, networks, and othercomputing resources. In certain embodiments, one or more of thecomponents of the data intake and query system 16 can be implemented ina remote distributed computing system. In this context, a remotedistributed computing system or cloud-based service can refer to aservice hosted by one more computing resources that are accessible toend users over a network, for example, by using a web browser or otherapplication on a client device to interface with the remote computingresources. For example, a service provider may provide a data intake andquery system 16 by managing computing resources configured to implementvarious aspects of the system (e.g., search head 210, indexers 206,worker nodes 3306, common storage 4602, ingested data buffer 4802,search process master 3302, query coordinator 3304, acceleration datastore 3308, etc.) and by providing access to the system to end users viaa network.

When implemented as a cloud-based service, various components of thesystem 108 can be implemented using containerization oroperating-system-level virtualization, or other virtualizationtechnique. For example, one or more components of the system 16 (e.g.,search head 210, indexers 206, worker nodes 3306, ingested data buffer4802, search process master 3302, query coordinator 3304, etc.) can beimplemented as separate software containers or container instances. Eachcontainer instance can have certain resources (e.g., memory, processor,etc.) of the underlying host computing system assigned to it, but mayshare the same operating system and may use the operating system'ssystem call interface. Each container may provide an isolated executionenvironment on the host system, such as by providing a memory space ofthe host system that is logically isolated from memory space of othercontainers. Further, each container may run the same or differentcomputer applications concurrently or separately, and may interact witheach other. Although reference is made herein to containerization andcontainer instances, it will be understood that other virtualizationtechniques can be used. For example, the components can be implementedusing virtual machines using full virtualization or paravirtualization,etc. Thus, where reference is made to “containerized” components, itshould be understood that such components may additionally oralternatively be implemented in other isolated execution environments,such as a virtual machine environment.

Likewise, the data repositories shown can represent physical and/orlogical data storage, including, e.g., storage area networks or otherdistributed storage systems. Moreover, in some embodiments theconnections between the components shown represent possible paths ofdata flow, rather than actual connections between hardware. While someexamples of possible connections are shown, any of the subset of thecomponents shown can communicate with any other subset of components invarious implementations.

Embodiments are also described above with reference to flow chartillustrations and/or block diagrams of methods, apparatus (systems) andcomputer program products. Each block of the flow chart illustrationsand/or block diagrams, and combinations of blocks in the flow chartillustrations and/or block diagrams, may be implemented by computerprogram instructions. Such instructions may be provided to a processorof a general purpose computer, special purpose computer,specially-equipped computer (e.g., comprising a high-performancedatabase server, a graphics subsystem, etc.) or other programmable dataprocessing apparatus to produce a machine, such that the instructions,which execute via the processor(s) of the computer or other programmabledata processing apparatus, create means for implementing the actsspecified in the flow chart and/or block diagram block or blocks. Thesecomputer program instructions may also be stored in a non-transitorycomputer-readable memory that can direct a computer or otherprogrammable data processing apparatus to operate in a particularmanner, such that the instructions stored in the computer-readablememory produce an article of manufacture including instruction meanswhich implement the acts specified in the flow chart and/or blockdiagram block or blocks. The computer program instructions may also beloaded to a computing device or other programmable data processingapparatus to cause operations to be performed on the computing device orother programmable apparatus to produce a computer implemented processsuch that the instructions which execute on the computing device orother programmable apparatus provide steps for implementing the actsspecified in the flow chart and/or block diagram block or blocks.

Any patents and applications and other references noted above, includingany that may be listed in accompanying filing papers, are incorporatedherein by reference. Aspects of the invention can be modified, ifnecessary, to employ the systems, functions, and concepts of the variousreferences described above to provide yet further implementations of theinvention. These and other changes can be made to the invention in lightof the above Detailed Description. While the above description describescertain examples of the invention, and describes the best modecontemplated, no matter how detailed the above appears in text, theinvention can be practiced in many ways. Details of the system may varyconsiderably in its specific implementation, while still beingencompassed by the invention disclosed herein. As noted above,particular terminology used when describing certain features or aspectsof the invention should not be taken to imply that the terminology isbeing redefined herein to be restricted to any specific characteristics,features, or aspects of the invention with which that terminology isassociated. In general, the terms used in the following claims shouldnot be construed to limit the invention to the specific examplesdisclosed in the specification, unless the above Detailed Descriptionsection explicitly defines such terms. Accordingly, the actual scope ofthe invention encompasses not only the disclosed examples, but also allequivalent ways of practicing or implementing the invention under theclaims.

To reduce the number of claims, certain aspects of the invention arepresented below in certain claim forms, but the applicant contemplatesother aspects of the invention in any number of claim forms. Any claimsintended to be treated under 35 U.S.C. § 112(f) will begin with thewords “means for,” but use of the term “for” in any other context is notintended to invoke treatment under 35 U.S.C. § 112(f). Accordingly, theapplicant reserves the right to pursue additional claims after filingthis application, in either this application or in a continuingapplication.

1. (canceled)
 2. A method, comprising: receiving one or more identifiersfor one or more computing devices associated with operation of one ormore worker nodes of a distributed computing framework associated with adata intake and query system; determining a computing device of the oneor more computing devices to operate a worker node of the one or moreworker nodes based at least in part on the one or more identifiers forthe one or more computing devices; and activating the worker node on theone or more computing devices based at least in part on determining theone or more computing devices to operate the worker node, wherein theworker node is configured to execute at least a portion of one or morequeries.
 3. The method of claim 2, wherein the one or more computingdevices comprise one or more computing devices of a data intake andquery system.
 4. The method of claim 2, further comprising receiving asearch command from a search head of the data intake and query system,wherein the one or more identifiers are associated with the searchcommand.
 5. The method of claim 4, wherein the search head receives theone or more identifiers.
 6. The method of claim 2, further comprisingreceiving a configuration file, wherein the configuration file comprisesthe one or more identifiers.
 7. The method of claim 6, wherein a searchhead of the data intake and query system distributes the configurationfile to other search heads of the data intake and query system.
 8. Themethod of claim 6, wherein the configuration file includes a secret keygenerated by a master of the distributed computing framework, whereinactivating the worker node comprises communicating, by the worker node,to the master, the secret key.
 9. The method of claim 2, wherein amaster of the distributed computing framework is in communication with aplurality of worker nodes of the distributed computing framework,wherein the plurality of worker nodes are operating in differentgeographical locations, and wherein the master is configured to, inresponse to a query, use worker nodes of the plurality of worker nodesoperating in a same geographical location as data for the query.
 10. Themethod of claim 2, wherein activating the worker node comprisesconfiguring the worker node to communicate with a master of thedistributed computing framework.
 11. The method of claim 2, furthercomprising: receiving information regarding the distributed computingframework; determining that the information does not identify the one ormore computing devices to operate the worker node; and deactivating theworker node on the one or more computing devices.
 12. The method ofclaim 2, further comprising activating an additional worker node of theone or more worker nodes on one or more second computing devices,wherein the one or more second computing devices are configured todedicate resources for operating the additional worker node.
 13. Themethod of claim 2, wherein the worker node is further configured toreceive a search command associated with the at least a portion of theone or more queries and obtain results of the at least a portion of theone or more queries based on the received search command.
 14. The methodof claim 2, further comprising: installing worker node software on theone or more computing devices; and executing the worker node software onthe one or more computing devices to launch the worker node on the oneor more computing devices.
 15. The method of claim 2, further comprisingreceiving a configuration file comprising communication information andthe one or more identifiers, wherein activating the worker nodecomprises establishing communication, between the worker node and amaster, using the communication information.
 16. The method of claim 2,further comprising receiving a configuration file comprisingcommunication information and the one or more identifiers, whereinactivating the worker node comprises establishing communication, betweenthe worker node and a master, using the communication information,wherein the master operates on a search head.
 17. The method of claim 2,further comprising: receiving a configuration file; and checking theconfiguration file for the one or more identifiers.
 18. The method ofclaim 2, wherein the one or more identifiers comprise a global uniqueidentifier (GUID) or an IP address for the one or more computingdevices.
 19. The method of claim 2, further comprising: receiving asearch command from a search head of the data intake and query system;installing master software on the search head; and executing the mastersoftware on the search head to launch a master of the distributedcomputing framework on the search head.
 20. A computing system,comprising: memory; and one or more processing devices coupled to thememory and configured to: receive one or more identifiers for one ormore computing devices associated with operation of one or more workernodes of a distributed computing framework associated with a data intakeand query system; determine a computing device of the one or morecomputing devices to operate a worker node of the one or more workernodes based at least in part on the one or more identifiers for the oneor more computing devices; and activate the worker node on the one ormore computing devices based at least in part on determining the one ormore computing devices to operate the worker node, wherein the workernode is configured to execute at least a portion of one or more queries.21. Non-transitory computer readable media comprisingcomputer-executable instructions that, when executed by a computingsystem, cause the computing system to: receive one or more identifiersfor one or more computing devices associated with operation of one ormore worker nodes of a distributed computing framework associated with adata intake and query system; determine a computing device of the one ormore computing devices to operate a worker node of the one or moreworker nodes based at least in part on the one or more identifiers forthe one or more computing devices; and activate the worker node on theone or more computing devices based at least in part on determining theone or more computing devices to operate the worker node, wherein theworker node is configured to execute at least a portion of one or morequeries